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This is the UTF-8 encoded README file associated with the page
    http://cblib.zib.de/download/


All instances of the Conic Bencmark Library (CBLIB) are available under this 
page via the HTTP protocol. Download via your browser (e.g., using a download
manager plugin), or via wget (e.g., VisualWget on Windows) as follows:

Download all instances
    wget -r -l1 -np http://cblib.zib.de/download/all/ -A *.cbf.gz

where
    -r  : recursive download (follow hyperlinks)
    -lX : set maximum recursion depth to X
    -np : do not follow hyperlinks to parents of the specified page

or download a subset of instances filtered by
    -A acclist --accept acclist
    -R rejlist --reject rejlist
       Specify comma-separated lists of file name suffixes or patterns to 
       accept or reject. Note that if any of the wildcard characters, *, ?,
       [ or ], appear in an element of acclist or rejlist, it will be 
       treated as a pattern, rather than a suffix.

    --accept-regex urlregex
    --reject-regex urlregex
        Specify a regular expression to accept or reject the complete URL.


After download, you can filter the instances further using grep-tools, e.g.,
all instances with integer variables and without linear matrix inequalities:
    zgrep -L "PSDCON" $(zgrep -l "INT" *.cbf.gz | xargs)

More advanced filters are implemented in the python scripts of
    https://github.com/HFriberg/cblib-base.


=============================================================================
External beam radiotherapy
=============================================================================
Contributor: Jagdish Ramakrishnan
    beam7, beam30

@techreport{Ramakrishnan2014,
author = {Ramakrishnan, Jagdish and Ferris, Michael C},
institution = {University of Wisconsin–Madison},
title = {{SOCP Radiotherapy Benchmark Test Case in CBLIB}},
url = {http://pages.discovery.wisc.edu/~jramakrishnan/radiotherapy\_socp\_cblib.pdf},
year = {2014}
}


=============================================================================
The chained singular function (academic)
=============================================================================
Contributor: Joachim Dahl
    chainsing-1000-1, chainsing-1000-2, chainsing-1000-3, chainsing-10000-1, chainsing-10000-2, chainsing-10000-3, chainsing-50000-1, chainsing-50000-2, chainsing-50000-3

@article{Conn1988,
author = {Conn, Andrew R. and Gould, Nicholas I. M. and Toint, Philippe L.},
journal = {Mathematics of Computation},
number = {182},
pages = {399--430},
title = {{Testing a class of methods for solving minimization problems with simple bounds on the variables}},
volume = {50},
year = {1988}
}

@article{Kobayashi2008,
author = {Kobayashi, Kazuhiro and Kim, Sunyoung and Kojima, Masakazu},
journal = {Journal of the Operations Research Society of Japan},
number = {3},
pages = {241--264},
title = {{Sparse second order cone programming formulations for convex optimization problems}},
volume = {51},
year = {2008}
}


=============================================================================
Design of FIR filters
=============================================================================
Contributor: Hans D. Mittelmann
    2013_dsNRL, 2013_firL1, 2013_firL1Linfalph, 2013_firL1Linfeps, 2013_firL2L1alph, 2013_firL2L1eps, 2013_firL2Linfalph, 2013_firL2Linfeps, 2013_firL2a, 2013_firLinf, 2013_wbNRL, 2013i_wbNRL

Optimal design of a delta-sigma ('ds' in name), a wideband ('wb' in name) or a nonlinear-phase ('fir' in name) FIR filters.

@article{Coleman2002,
author = {Coleman, Jeffrey O. and Scholnik, Dan P. and Brandriss, Josef J.},
journal = {Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002.},
pages = {341--345},
publisher = {Ieee},
title = {{A specification language for the optimal design of exotic FIR filters with second-order cone programs}},
volume = {1},
year = {2002}
}


=============================================================================
Minimizing geometric distortion
=============================================================================
Contributor: Zohar Levi
    strictmin_2D_43_dual, strictmin_2D_43_primal

Linf-minimization based on the as-rigid-as-possible ARAP energy.

@inproceedings{Levi2014,
author = {Levi, Zohar and Zorin, Denis},
booktitle = {ACM SIGGRAPH Asia},
title = {{Strict Minimizers For Geometric Optimization}},
year = {2014}
}


=============================================================================
Calibration of antenna arrays
=============================================================================
Contributor: Henrik A. Friberg
    nb, nb_L1, nb_L2, nb_L2_bessel

Calibration of antenna arrays, suppressing signals that do not come from a chosen direction.

@inproceedings{Coleman1999,
author = {Coleman, Jeffrey O. and Vanderbei, Robert J.},
booktitle = {The 1999 Midwest Symposium on Circuits and Systems},
title = {{Random-process formulation of computationally efficient performance measures for wideband arrays in the far field}},
year = {1999}
}


=============================================================================
Job scheduling
=============================================================================
Contributor: Henrik A. Friberg
    sched_50_50_orig, sched_50_50_scaled, sched_100_50_orig, sched_100_50_scaled, sched_100_100_orig, sched_100_100_scaled, sched_200_100_orig, sched_200_100_scaled

Job scheduling with stochastic processing times on parallel unrelated machines.

@article{Skutella2001,
author = {Skutella, Martin},
doi = {10.1145/375827.375840},
issn = {00045411},
journal = {Journal of the ACM},
month = mar,
number = {2},
pages = {206--242},
title = {{Convex quadratic and semidefinite programming relaxations in scheduling}},
volume = {48},
year = {2001}
}


=============================================================================
Collapse analysis
=============================================================================
Contributor: Henrik A. Friberg
    nql30, nql60, nql90, nql180, qssp30, qssp60, qssp90, qssp180
Contributor: Morten A. Herfelt
    db-joint-soerensen, db-plane-strain-prism, db-plate-yield-line, db-plate-yield-line-fox, db-shear-wall
Contributor: Morten A. Herfelt
    joint_FC_5, joint_FC_7, joint_FC_8, joint_FC_9, joint_FC_12, joint_HO_01, joint_HO_02, joint_HO_03, joint_HO_04, joint_HO_05, joint_HO_12, joint_HO_13, joint_HO_14, joint_HO_18, joint_HO_23, joint_HO_24, joint_HO_25, joint_HO_26, joint_HO_27, joint_HO_28, joint_HO_29

Numerical limit analysis:
    of loaded plastic plates [Andersen1998, Christiansen1999]:
        * using the plane strain model ('nql' in name).
        * using the supported plate model ('qssp' in name). 
        * in various case studies ('db' in name).
    of keyed shear joints in precast concrete structures [Herfelt2015]:
        * using plane stress model ('joint' in name).

@article{Andersen1998,
author = {Andersen, Knud D. and Christiansen, Edmund and Overton, Michael L.},
doi = {10.1137/S1064827594275303},
isbn = {1064827594},
issn = {1064-8275},
journal = {SIAM Journal on Scientific Computing},
month = may,
number = {3},
pages = {1046--1062},
title = {{Computing Limit Loads by Minimizing a Sum of Norms}},
volume = {19},
year = {1998}
}

@article{Christiansen1999,
author = {Christiansen, Edmund and Andersen, Knud D.},
doi = {10.1002/(SICI)1097-0207(19991120)46:8<1185::AID-NME743>3.0.CO;2-N},
issn = {0029-5981},
journal = {International Journal for Numerical Methods in Engineering},
month = nov,
number = {8},
pages = {1185--1202},
title = {{Computation of collapse states with von Mises type yield condition}},
volume = {46},
year = {1999}
}

@article{Herfelt2015,
author = {Herfelt, Morten A. and Poulsen, Peter N. and Hoang, Linh C. and Jensen, Jesper F.},
doi = {10.1002/suco.201500161},
journal = {Structural Concrete},
title = {{Numerical rigid plastic modelling of keyed shear joints in concrete structures}},
year = {2015}
}


=============================================================================
Retrofit-synthesis of process networks
=============================================================================
Contributor: Miles Lubin
    synthes1, synthes2, synthes3
Contributor: Miles Lubin
    syn05h, syn05m, syn05m02h, syn05m02m, syn05m03h, syn05m03m, syn05m04h, syn05m04m, syn10h, syn10m, syn10m02h, syn10m02m, syn10m03h, syn10m03m, syn10m04h, syn10m04m, syn15h, syn15m, syn15m02h, syn15m02m, syn15m03h, syn15m03m, syn15m04h, syn15m04m, syn20h, syn20m, syn20m02h, syn20m02m, syn20m03h, syn20m03m, syn20m04h, syn20m04m, syn30h, syn30m, syn30m02h, syn30m02m, syn30m03h, syn30m03m, syn30m04h, syn30m04m, syn40h, syn40m, syn40m02h, syn40m02m, syn40m03h, syn40m03m, syn40m04h, syn40m04m
Contributor: Miles Lubin
    rsyn0805h, rsyn0805m02h, rsyn0805m02m, rsyn0805m03h, rsyn0805m03m, rsyn0805m04h, rsyn0805m04m, rsyn0805m, rsyn0810h, rsyn0810m02h, rsyn0810m02m, rsyn0810m03h, rsyn0810m03m, rsyn0810m04h, rsyn0810m04m, rsyn0810m, rsyn0815h, rsyn0815m02h, rsyn0815m02m, rsyn0815m03h, rsyn0815m03m, rsyn0815m04h, rsyn0815m04m, rsyn0815m, rsyn0820h, rsyn0820m02h, rsyn0820m02m, rsyn0820m03h, rsyn0820m03m, rsyn0820m04h, rsyn0820m04m, rsyn0820m, rsyn0830h, rsyn0830m02h, rsyn0830m02m, rsyn0830m03h, rsyn0830m03m, rsyn0830m04h, rsyn0830m04m, rsyn0830m, rsyn0840h, rsyn0840m02h, rsyn0840m02m, rsyn0840m03h, rsyn0840m03m, rsyn0840m04h, rsyn0840m04m, rsyn0840m

Simultaneous redesign part of an existing plant and synthesize (from scratch) part of a new one. Determines whether certain units should be included in the design of the new plant, and whether certain modifications such as improvements in yield, capacity and energy reduction should be performed on the existing plant. The economic potential is maximized given a certain time horizon and limited capital investments.

Big-M formulations ('syn' prefix) [Duran1986,Turkay1996] and convex hull formulations ('rsyn' prefix) [Sawaya2007]. Description modified from [Bonami2008].

Converted from MINLPLib (http://www.minlplib.org/). Tiny epsilon constants used for avoiding division by zero have been removed since these are not needed in the conic formulation.

Originally available through the CMU-IBM MINLP solver project page: http://egon.cheme.cmu.edu/ibm/page.htm.

@article{Duran1986,
author = {Duran, Marco A. and Grossmann, Ignacio E.},
journal = {Mathematical Programming},
number = {3},
pages = {307--339},
title = {{An outer-approximation algorithm for a class of mixed-integer nonlinear programs}},
volume = {36},
year = {1986}
}

@article{Turkay1996,
author = {T{\"{u}}rkay, Metin and Grossmann, Ignacio E.},
journal = {Computers and Chemical Engineering},
number = {8},
pages = {959--978},
title = {{Logic-based MINLP algorithms for the optimal synthesis of process networks}},
volume = {20},
year = {1996}
}

@article{Sawaya2007,
author = {Sawaya, Nicolas W. and Grossmann, Ignacio E.},
doi = {10.1016/j.compchemeng.2006.08.002},
issn = {00981354},
journal = {Computers and Chemical Engineering},
number = {7},
pages = {856--866},
title = {{Computational implementation of non-linear convex hull reformulation}},
volume = {31},
year = {2007}
}

@article{Bonami2008,
author = {Bonami, Pierre and Biegler, Lorenz T. and Conn, Andrew R. and Cornu{\'e}Jols, G{\'e}Rard and Grossmann, Ignacio E. and Laird, Carl D. and Lee, Jon and Lodi, Andrea and Margot, Fran\c{c}Ois and Sawaya, Nicolas and W\"{a}Chter, Andreas},
title = {An Algorithmic Framework for Convex Mixed Integer Nonlinear Programs},
journal = {Discrete Optimization},
volume = {5},
number = {2},
year = {2008},
issn = {1572-5286},
pages = {186--204},
doi = {10.1016/j.disopt.2006.10.011},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
} 


=============================================================================
Trim-loss minimization
=============================================================================
Contributor: Miles Lubin
    tls12, tls2, tls4, tls5, tls6, tls7

Cutting stock problems.

Converted from MINLPLib (http://www.minlplib.org/).

@incollection{Lubin2016,
author = {Lubin, Miles and Yamangil, Emre and Bent, Russel and Vielma, Juan Pablo},
booktitle = {Integer Programming and Combinatorial Optimization: 18th International Conference},
pages = {102--113},
publisher = {Springer International Publishing},
title = {{Extended Formulations in Mixed-integer Convex Programming}},
year = {2016}
}


=============================================================================
Chance constrained knapsack problem
=============================================================================
Contributor: Alper Atamtürk
    ck_n25_m10_o1_1, ck_n25_m10_o1_2, ck_n25_m10_o1_3, ck_n25_m10_o1_4, ck_n25_m10_o1_5, ck_n25_m10_o3_1, ck_n25_m10_o3_2, ck_n25_m10_o3_3, ck_n25_m10_o3_4, ck_n25_m10_o3_5, ck_n25_m10_o5_1, ck_n25_m10_o5_2, ck_n25_m10_o5_3, ck_n25_m10_o5_4, ck_n25_m10_o5_5, ck_n25_m20_o1_1, ck_n25_m20_o1_2, ck_n25_m20_o1_3, ck_n25_m20_o1_4, ck_n25_m20_o1_5, ck_n25_m20_o3_1, ck_n25_m20_o3_2, ck_n25_m20_o3_3, ck_n25_m20_o3_4, ck_n25_m20_o3_5, ck_n25_m20_o5_1, ck_n25_m20_o5_2, ck_n25_m20_o5_3, ck_n25_m20_o5_4, ck_n25_m20_o5_5, ck_n50_m10_o1_1, ck_n50_m10_o1_2, ck_n50_m10_o1_3, ck_n50_m10_o1_4, ck_n50_m10_o1_5, ck_n50_m10_o3_1, ck_n50_m10_o3_2, ck_n50_m10_o3_3, ck_n50_m10_o3_4, ck_n50_m10_o3_5, ck_n50_m10_o5_1, ck_n50_m10_o5_2, ck_n50_m10_o5_3, ck_n50_m10_o5_4, ck_n50_m10_o5_5, ck_n50_m20_o1_1, ck_n50_m20_o1_2, ck_n50_m20_o1_3, ck_n50_m20_o1_4, ck_n50_m20_o1_5, ck_n50_m20_o3_1, ck_n50_m20_o3_2, ck_n50_m20_o3_3, ck_n50_m20_o3_4, ck_n50_m20_o3_5, ck_n50_m20_o5_1, ck_n50_m20_o5_2, ck_n50_m20_o5_3, ck_n50_m20_o5_4, ck_n50_m20_o5_5, ck_n75_m10_o1_1, ck_n75_m10_o1_2, ck_n75_m10_o1_3, ck_n75_m10_o1_4, ck_n75_m10_o1_5, ck_n75_m10_o3_1, ck_n75_m10_o3_2, ck_n75_m10_o3_3, ck_n75_m10_o3_4, ck_n75_m10_o3_5, ck_n75_m10_o5_1, ck_n75_m10_o5_2, ck_n75_m10_o5_3, ck_n75_m10_o5_4, ck_n75_m10_o5_5, ck_n75_m20_o1_1, ck_n75_m20_o1_2, ck_n75_m20_o1_3, ck_n75_m20_o1_4, ck_n75_m20_o1_5, ck_n75_m20_o3_1, ck_n75_m20_o3_2, ck_n75_m20_o3_3, ck_n75_m20_o3_4, ck_n75_m20_o3_5, ck_n75_m20_o5_1, ck_n75_m20_o5_2, ck_n75_m20_o5_3, ck_n75_m20_o5_4, ck_n75_m20_o5_5

@article{Atamturk2009,
author = {Atamt\"{u}rk, Alper and Narayanan, Vishnu},
doi = {10.1016/j.disopt.2009.03.002},
issn = {15725286},
journal = {Discrete Optimization},
number = {4},
pages = {333--344},
publisher = {Elsevier B.V.},
title = {{The submodular knapsack polytope}},
volume = {6},
year = {2009}
}


=============================================================================
Minimum Steiner tree problem
=============================================================================
Contributor: Henrik A. Friberg
    estein4_A, estein4_B, estein4_C, estein4_nr22, estein5_A, estein5_B, estein5_C, estein5_nr1, estein5_nr21

@phdthesis{Drewes2009,
author = {Drewes, Sarah},
number = {April 2009},
pages = {1--203},
school = {Technical University Darmstadt},
title = {{Mixed Integer Second Order Cone Programming}},
year = {2009}
}


=============================================================================
Pipe routing
=============================================================================
Contributor: Jakob Schelbert
    integrated_KleinesBeispiel_02_6, integrated_Timo-Test-ST-01_1

The problem of routing high pressure steam pipes in a power plant.

@phdthesis{Schelbert2015,
author = {Schelbert, Jakob},
school = {FAU Erlangen N\"{u}rnberg},
title = {{Discrete Approaches for Optimal Routing of High Pressure Pipes}},
year = {2015}
}


=============================================================================
Portfolio optimization
=============================================================================
Contributor: Rune Sandvik
    classical_20_0, classical_20_1, classical_20_2, classical_20_3, classical_20_4, classical_20_5, classical_20_6, classical_20_7, classical_20_8, classical_20_9, classical_20_10, classical_20_11, classical_20_12, classical_20_13, classical_20_14, classical_20_15, classical_20_16, classical_20_17, classical_20_18, classical_20_19, classical_20_20, classical_20_21, classical_20_22, classical_20_23, classical_20_24, classical_20_25, classical_20_26, classical_20_27, classical_20_28, classical_20_29, classical_20_30, classical_20_31, classical_20_32, classical_20_34, classical_20_35, classical_20_36, classical_20_37, classical_20_38, classical_20_39, classical_20_40, classical_20_41, classical_20_42, classical_20_43, classical_20_44, classical_20_45, classical_20_46, classical_20_47, classical_20_48, classical_20_49, classical_20_50, classical_20_51, classical_20_52, classical_20_53, classical_20_54, classical_20_55, classical_20_56, classical_20_57, classical_20_58, classical_20_59, classical_20_60, classical_20_61, classical_20_62, classical_20_63, classical_20_64, classical_20_65, classical_20_66, classical_20_67, classical_20_68, classical_20_69, classical_20_70, classical_20_71, classical_20_72, classical_20_73, classical_20_74, classical_20_75, classical_20_76, classical_20_77, classical_20_78, classical_20_79, classical_20_80, classical_20_81, classical_20_82, classical_20_83, classical_20_84, classical_20_85, classical_20_86, classical_20_87, classical_20_88, classical_20_89, classical_20_90, classical_20_91, classical_20_92, classical_20_93, classical_20_94, classical_20_95, classical_20_96, classical_20_97, classical_20_98, classical_20_99, classical_30_0, classical_30_1, classical_30_2, classical_30_3, classical_30_4, classical_30_5, classical_30_6, classical_30_7, classical_30_8, classical_30_9, classical_30_10, classical_30_11, classical_30_12, classical_30_13, classical_30_14, classical_30_15, classical_30_16, classical_30_17, classical_30_18, classical_30_19, classical_30_20, classical_30_21, classical_30_22, classical_30_23, classical_30_24, classical_30_25, classical_30_26, classical_30_27, classical_30_28, classical_30_29, classical_30_30, classical_30_31, classical_30_32, classical_30_33, classical_30_34, classical_30_35, classical_30_36, classical_30_37, classical_30_38, classical_30_39, classical_30_40, classical_30_41, classical_30_42, classical_30_43, classical_30_44, classical_30_45, classical_30_46, classical_30_47, classical_30_48, classical_30_49, classical_30_50, classical_30_51, classical_30_52, classical_30_53, classical_30_54, classical_30_55, classical_30_56, classical_30_57, classical_30_58, classical_30_59, classical_30_60, classical_30_61, classical_30_62, classical_30_63, classical_30_64, classical_30_65, classical_30_66, classical_30_67, classical_30_68, classical_30_69, classical_30_70, classical_30_71, classical_30_72, classical_30_73, classical_30_74, classical_30_75, classical_30_76, classical_30_77, classical_30_78, classical_30_79, classical_30_80, classical_30_81, classical_30_82, classical_30_83, classical_30_84, classical_30_85, classical_30_86, classical_30_87, classical_30_88, classical_30_89, classical_30_90, classical_30_91, classical_30_92, classical_30_93, classical_30_94, classical_30_95, classical_30_96, classical_30_97, classical_30_98, classical_30_99, classical_40_0, classical_40_1, classical_40_2, classical_40_3, classical_40_4, classical_40_5, classical_40_6, classical_40_7, classical_40_8, classical_40_9, classical_40_10, classical_40_11, classical_40_12, classical_40_13, classical_40_14, classical_40_15, classical_40_16, classical_40_17, classical_40_18, classical_40_19, classical_40_20, classical_40_21, classical_40_22, classical_40_23, classical_40_24, classical_40_25, classical_40_26, classical_40_27, classical_40_28, classical_40_29, classical_40_30, classical_40_31, classical_40_32, classical_40_33, classical_40_34, classical_40_35, classical_40_36, classical_40_37, classical_40_38, classical_40_39, classical_40_40, classical_40_41, classical_40_42, classical_40_43, classical_40_44, classical_40_45, classical_40_46, classical_40_47, classical_40_48, classical_40_49, classical_40_50, classical_40_51, classical_40_52, classical_40_53, classical_40_54, classical_40_55, classical_40_56, classical_40_57, classical_40_58, classical_40_59, classical_40_60, classical_40_61, classical_40_62, classical_40_63, classical_40_64, classical_40_65, classical_40_66, classical_40_67, classical_40_68, classical_40_69, classical_40_70, classical_40_71, classical_40_72, classical_40_73, classical_40_74, classical_40_75, classical_40_76, classical_40_77, classical_40_78, classical_40_79, classical_40_80, classical_40_81, classical_40_82, classical_40_83, classical_40_84, classical_40_85, classical_40_86, classical_40_87, classical_40_88, classical_40_89, classical_40_90, classical_40_91, classical_40_92, classical_40_93, classical_40_94, classical_40_95, classical_40_96, classical_40_97, classical_40_98, classical_40_99, classical_50_0, classical_50_1, classical_50_2, classical_50_3, classical_50_4, classical_50_5, classical_50_6, classical_50_7, classical_50_8, classical_50_9, classical_50_10, classical_50_11, classical_50_12, classical_50_13, classical_50_14, classical_50_15, classical_50_16, classical_50_17, classical_50_18, classical_50_19, classical_50_20, classical_50_21, classical_50_22, classical_50_23, classical_50_24, classical_50_25, classical_50_26, classical_50_27, classical_50_28, classical_50_29, classical_50_30, classical_50_31, classical_50_32, classical_50_33, classical_50_34, classical_50_35, classical_50_36, classical_50_37, classical_50_38, classical_50_39, classical_50_40, classical_50_41, classical_50_42, classical_50_43, classical_50_44, classical_50_45, classical_50_46, classical_50_47, classical_50_48, classical_50_49, classical_50_50, classical_50_51, classical_50_52, classical_50_53, classical_50_54, classical_50_55, classical_50_56, classical_50_57, classical_50_58, classical_50_59, classical_50_60, classical_50_61, classical_50_62, classical_50_63, classical_50_64, classical_50_65, classical_50_66, classical_50_67, classical_50_68, classical_50_69, classical_50_70, classical_50_71, classical_50_72, classical_50_73, classical_50_74, classical_50_75, classical_50_76, classical_50_77, classical_50_78, classical_50_79, classical_50_80, classical_50_81, classical_50_82, classical_50_83, classical_50_84, classical_50_85, classical_50_86, classical_50_87, classical_50_88, classical_50_89, classical_50_90, classical_50_91, classical_50_92, classical_50_93, classical_50_94, classical_50_95, classical_50_96, classical_50_97, classical_50_98, classical_50_99, classical_200_0, classical_200_1, classical_200_2, classical_200_3, classical_200_4, classical_200_5, classical_200_6, classical_200_7, classical_200_8, classical_200_9, robust_20_0, robust_20_1, robust_20_2, robust_20_3, robust_20_4, robust_20_5, robust_20_6, robust_20_7, robust_20_8, robust_20_9, robust_20_10, robust_20_11, robust_20_12, robust_20_13, robust_20_14, robust_20_15, robust_20_16, robust_20_17, robust_20_18, robust_20_19, robust_20_20, robust_20_21, robust_20_22, robust_20_23, robust_20_24, robust_20_25, robust_20_26, robust_20_27, robust_20_28, robust_20_29, robust_20_30, robust_20_31, robust_20_32, robust_20_33, robust_20_34, robust_20_35, robust_20_36, robust_20_37, robust_20_38, robust_20_39, robust_20_40, robust_20_41, robust_20_42, robust_20_43, robust_20_44, robust_20_45, robust_20_46, robust_20_47, robust_20_48, robust_20_49, robust_20_50, robust_20_51, robust_20_52, robust_20_53, robust_20_54, robust_20_55, robust_20_56, robust_20_57, robust_20_58, robust_20_59, robust_20_60, robust_20_61, robust_20_62, robust_20_63, robust_20_64, robust_20_65, robust_20_66, robust_20_67, robust_20_68, robust_20_69, robust_20_70, robust_20_71, robust_20_72, robust_20_73, robust_20_74, robust_20_75, robust_20_76, robust_20_77, robust_20_78, robust_20_79, robust_20_80, robust_20_81, robust_20_82, robust_20_83, robust_20_84, robust_20_85, robust_20_86, robust_20_87, robust_20_88, robust_20_89, robust_20_90, robust_20_91, robust_20_92, robust_20_93, robust_20_94, robust_20_95, robust_20_96, robust_20_97, robust_20_98, robust_20_99, robust_30_0, robust_30_1, robust_30_2, robust_30_3, robust_30_4, robust_30_5, robust_30_6, robust_30_7, robust_30_8, robust_30_9, robust_30_10, robust_30_11, robust_30_12, robust_30_13, robust_30_14, robust_30_15, robust_30_16, robust_30_17, robust_30_18, robust_30_19, robust_30_20, robust_30_21, robust_30_22, robust_30_23, robust_30_24, robust_30_25, robust_30_26, robust_30_27, robust_30_28, robust_30_29, robust_30_30, robust_30_31, robust_30_32, robust_30_33, robust_30_34, robust_30_35, robust_30_36, robust_30_37, robust_30_38, robust_30_39, robust_30_40, robust_30_41, robust_30_42, robust_30_43, robust_30_44, robust_30_45, robust_30_46, robust_30_47, robust_30_48, robust_30_49, robust_30_50, robust_30_51, robust_30_52, robust_30_53, robust_30_54, robust_30_55, robust_30_56, robust_30_57, robust_30_58, robust_30_59, robust_30_60, robust_30_61, robust_30_62, robust_30_63, robust_30_64, robust_30_65, robust_30_66, robust_30_67, robust_30_68, robust_30_69, robust_30_70, robust_30_71, robust_30_72, robust_30_73, robust_30_74, robust_30_75, robust_30_76, robust_30_77, robust_30_78, robust_30_79, robust_30_80, robust_30_81, robust_30_82, robust_30_83, robust_30_84, robust_30_85, robust_30_86, robust_30_87, robust_30_88, robust_30_89, robust_30_90, robust_30_91, robust_30_92, robust_30_93, robust_30_94, robust_30_95, robust_30_96, robust_30_97, robust_30_98, robust_30_99, robust_40_0, robust_40_1, robust_40_2, robust_40_3, robust_40_4, robust_40_5, robust_40_6, robust_40_7, robust_40_8, robust_40_9, robust_40_10, robust_40_11, robust_40_12, robust_40_13, robust_40_14, robust_40_15, robust_40_16, robust_40_17, robust_40_18, robust_40_19, robust_40_20, robust_40_21, robust_40_22, robust_40_23, robust_40_24, robust_40_25, robust_40_26, robust_40_27, robust_40_28, robust_40_29, robust_40_30, robust_40_31, robust_40_32, robust_40_33, robust_40_34, robust_40_35, robust_40_36, robust_40_37, robust_40_38, robust_40_39, robust_40_40, robust_40_41, robust_40_42, robust_40_43, robust_40_44, robust_40_45, robust_40_46, robust_40_47, robust_40_48, robust_40_49, robust_40_50, robust_40_51, robust_40_52, robust_40_53, robust_40_54, robust_40_55, robust_40_56, robust_40_57, robust_40_58, robust_40_59, robust_40_60, robust_40_61, robust_40_62, robust_40_63, robust_40_64, robust_40_65, robust_40_66, robust_40_67, robust_40_68, robust_40_69, robust_40_70, robust_40_71, robust_40_72, robust_40_73, robust_40_74, robust_40_75, robust_40_76, robust_40_77, robust_40_78, robust_40_79, robust_40_80, robust_40_81, robust_40_82, robust_40_83, robust_40_84, robust_40_85, robust_40_86, robust_40_87, robust_40_88, robust_40_89, robust_40_90, robust_40_91, robust_40_92, robust_40_93, robust_40_94, robust_40_95, robust_40_96, robust_40_97, robust_40_98, robust_40_99, robust_50_0, robust_50_1, robust_50_2, robust_50_3, robust_50_4, robust_50_5, robust_50_6, robust_50_7, robust_50_8, robust_50_9, robust_50_10, robust_50_11, robust_50_12, robust_50_13, robust_50_14, robust_50_15, robust_50_16, robust_50_17, robust_50_18, robust_50_19, robust_50_20, robust_50_21, robust_50_22, robust_50_23, robust_50_24, robust_50_25, robust_50_26, robust_50_27, robust_50_28, robust_50_29, robust_50_30, robust_50_31, robust_50_32, robust_50_33, robust_50_34, robust_50_35, robust_50_36, robust_50_37, robust_50_38, robust_50_39, robust_50_40, robust_50_41, robust_50_42, robust_50_43, robust_50_44, robust_50_45, robust_50_46, robust_50_47, robust_50_48, robust_50_49, robust_50_50, robust_50_51, robust_50_52, robust_50_53, robust_50_54, robust_50_55, robust_50_56, robust_50_57, robust_50_58, robust_50_59, robust_50_60, robust_50_61, robust_50_62, robust_50_63, robust_50_64, robust_50_65, robust_50_66, robust_50_67, robust_50_68, robust_50_69, robust_50_70, robust_50_71, robust_50_72, robust_50_73, robust_50_74, robust_50_75, robust_50_76, robust_50_77, robust_50_78, robust_50_79, robust_50_80, robust_50_81, robust_50_82, robust_50_83, robust_50_84, robust_50_85, robust_50_86, robust_50_87, robust_50_88, robust_50_89, robust_50_90, robust_50_91, robust_50_92, robust_50_93, robust_50_94, robust_50_95, robust_50_96, robust_50_97, robust_50_98, robust_50_99, robust_100_0, robust_100_1, robust_100_2, robust_100_3, robust_100_4, robust_100_5, robust_100_6, robust_100_7, robust_100_8, robust_100_9, robust_200_0, robust_200_1, robust_200_2, robust_200_3, robust_200_4, robust_200_5, robust_200_6, robust_200_7, robust_200_8, robust_200_9, shortfall_20_0, shortfall_20_1, shortfall_20_2, shortfall_20_3, shortfall_20_4, shortfall_20_5, shortfall_20_6, shortfall_20_7, shortfall_20_8, shortfall_20_9, shortfall_20_10, shortfall_20_11, shortfall_20_12, shortfall_20_13, shortfall_20_14, shortfall_20_15, shortfall_20_16, shortfall_20_17, shortfall_20_18, shortfall_20_19, shortfall_20_20, shortfall_20_21, shortfall_20_22, shortfall_20_23, shortfall_20_24, shortfall_20_25, shortfall_20_26, shortfall_20_27, shortfall_20_28, shortfall_20_29, shortfall_20_30, shortfall_20_31, shortfall_20_32, shortfall_20_33, shortfall_20_34, shortfall_20_35, shortfall_20_36, shortfall_20_37, shortfall_20_38, shortfall_20_39, shortfall_20_40, shortfall_20_41, shortfall_20_42, shortfall_20_43, shortfall_20_44, shortfall_20_45, shortfall_20_46, shortfall_20_47, shortfall_20_48, shortfall_20_49, shortfall_20_50, shortfall_20_51, shortfall_20_52, shortfall_20_53, shortfall_20_54, shortfall_20_55, shortfall_20_56, shortfall_20_57, shortfall_20_58, shortfall_20_59, shortfall_20_60, shortfall_20_61, shortfall_20_62, shortfall_20_63, shortfall_20_64, shortfall_20_65, shortfall_20_66, shortfall_20_67, shortfall_20_68, shortfall_20_69, shortfall_20_70, shortfall_20_71, shortfall_20_72, shortfall_20_73, shortfall_20_74, shortfall_20_75, shortfall_20_76, shortfall_20_77, shortfall_20_78, shortfall_20_79, shortfall_20_80, shortfall_20_81, shortfall_20_82, shortfall_20_83, shortfall_20_84, shortfall_20_85, shortfall_20_86, shortfall_20_87, shortfall_20_88, shortfall_20_89, shortfall_20_90, shortfall_20_91, shortfall_20_92, shortfall_20_93, shortfall_20_94, shortfall_20_95, shortfall_20_96, shortfall_20_97, shortfall_20_98, shortfall_20_99, shortfall_30_0, shortfall_30_1, shortfall_30_2, shortfall_30_3, shortfall_30_4, shortfall_30_5, shortfall_30_6, shortfall_30_7, shortfall_30_8, shortfall_30_9, shortfall_30_10, shortfall_30_11, shortfall_30_12, shortfall_30_13, shortfall_30_14, shortfall_30_15, shortfall_30_16, shortfall_30_17, shortfall_30_18, shortfall_30_19, shortfall_30_20, shortfall_30_21, shortfall_30_22, shortfall_30_23, shortfall_30_24, shortfall_30_25, shortfall_30_26, shortfall_30_27, shortfall_30_28, shortfall_30_29, shortfall_30_30, shortfall_30_31, shortfall_30_32, shortfall_30_33, shortfall_30_34, shortfall_30_35, shortfall_30_36, shortfall_30_37, shortfall_30_38, shortfall_30_39, shortfall_30_40, shortfall_30_41, shortfall_30_42, shortfall_30_43, shortfall_30_44, shortfall_30_45, shortfall_30_46, shortfall_30_47, shortfall_30_48, shortfall_30_49, shortfall_30_50, shortfall_30_51, shortfall_30_52, shortfall_30_53, shortfall_30_54, shortfall_30_55, shortfall_30_56, shortfall_30_57, shortfall_30_58, shortfall_30_59, shortfall_30_60, shortfall_30_61, shortfall_30_62, shortfall_30_63, shortfall_30_64, shortfall_30_65, shortfall_30_66, shortfall_30_67, shortfall_30_68, shortfall_30_69, shortfall_30_70, shortfall_30_71, shortfall_30_72, shortfall_30_73, shortfall_30_74, shortfall_30_75, shortfall_30_76, shortfall_30_77, shortfall_30_78, shortfall_30_79, shortfall_30_80, shortfall_30_81, shortfall_30_82, shortfall_30_83, shortfall_30_84, shortfall_30_85, shortfall_30_86, shortfall_30_87, shortfall_30_88, shortfall_30_89, shortfall_30_90, shortfall_30_91, shortfall_30_92, shortfall_30_93, shortfall_30_94, shortfall_30_95, shortfall_30_96, shortfall_30_97, shortfall_30_98, shortfall_30_99, shortfall_40_0, shortfall_40_1, shortfall_40_2, shortfall_40_3, shortfall_40_4, shortfall_40_5, shortfall_40_6, shortfall_40_7, shortfall_40_8, shortfall_40_9, shortfall_40_10, shortfall_40_11, shortfall_40_12, shortfall_40_13, shortfall_40_14, shortfall_40_15, shortfall_40_16, shortfall_40_17, shortfall_40_18, shortfall_40_19, shortfall_40_20, shortfall_40_21, shortfall_40_22, shortfall_40_23, shortfall_40_24, shortfall_40_25, shortfall_40_26, shortfall_40_27, shortfall_40_28, shortfall_40_29, shortfall_40_30, shortfall_40_31, shortfall_40_32, shortfall_40_33, shortfall_40_34, shortfall_40_35, shortfall_40_36, shortfall_40_37, shortfall_40_38, shortfall_40_39, shortfall_40_40, shortfall_40_41, shortfall_40_42, shortfall_40_43, shortfall_40_44, shortfall_40_45, shortfall_40_46, shortfall_40_47, shortfall_40_48, shortfall_40_49, shortfall_40_50, shortfall_40_51, shortfall_40_52, shortfall_40_53, shortfall_40_54, shortfall_40_55, shortfall_40_56, shortfall_40_57, shortfall_40_58, shortfall_40_59, shortfall_40_60, shortfall_40_61, shortfall_40_62, shortfall_40_63, shortfall_40_64, shortfall_40_65, shortfall_40_66, shortfall_40_67, shortfall_40_68, shortfall_40_69, shortfall_40_70, shortfall_40_71, shortfall_40_72, shortfall_40_73, shortfall_40_74, shortfall_40_75, shortfall_40_76, shortfall_40_77, shortfall_40_78, shortfall_40_79, shortfall_40_80, shortfall_40_81, shortfall_40_82, shortfall_40_83, shortfall_40_84, shortfall_40_85, shortfall_40_86, shortfall_40_87, shortfall_40_88, shortfall_40_89, shortfall_40_90, shortfall_40_91, shortfall_40_92, shortfall_40_93, shortfall_40_94, shortfall_40_95, shortfall_40_96, shortfall_40_97, shortfall_40_98, shortfall_40_99, shortfall_50_0, shortfall_50_1, shortfall_50_2, shortfall_50_3, shortfall_50_4, shortfall_50_5, shortfall_50_6, shortfall_50_7, shortfall_50_8, shortfall_50_9, shortfall_50_10, shortfall_50_11, shortfall_50_12, shortfall_50_13, shortfall_50_14, shortfall_50_15, shortfall_50_16, shortfall_50_17, shortfall_50_18, shortfall_50_19, shortfall_50_20, shortfall_50_21, shortfall_50_22, shortfall_50_23, shortfall_50_24, shortfall_50_25, shortfall_50_26, shortfall_50_27, shortfall_50_28, shortfall_50_29, shortfall_50_30, shortfall_50_31, shortfall_50_32, shortfall_50_33, shortfall_50_34, shortfall_50_35, shortfall_50_36, shortfall_50_37, shortfall_50_38, shortfall_50_39, shortfall_50_40, shortfall_50_41, shortfall_50_42, shortfall_50_43, shortfall_50_44, shortfall_50_45, shortfall_50_46, shortfall_50_47, shortfall_50_48, shortfall_50_49, shortfall_50_50, shortfall_50_51, shortfall_50_52, shortfall_50_53, shortfall_50_54, shortfall_50_55, shortfall_50_56, shortfall_50_57, shortfall_50_58, shortfall_50_59, shortfall_50_60, shortfall_50_61, shortfall_50_62, shortfall_50_63, shortfall_50_64, shortfall_50_65, shortfall_50_66, shortfall_50_67, shortfall_50_68, shortfall_50_69, shortfall_50_70, shortfall_50_71, shortfall_50_72, shortfall_50_73, shortfall_50_74, shortfall_50_75, shortfall_50_76, shortfall_50_77, shortfall_50_78, shortfall_50_79, shortfall_50_80, shortfall_50_81, shortfall_50_82, shortfall_50_83, shortfall_50_84, shortfall_50_85, shortfall_50_86, shortfall_50_87, shortfall_50_88, shortfall_50_89, shortfall_50_90, shortfall_50_91, shortfall_50_92, shortfall_50_93, shortfall_50_94, shortfall_50_95, shortfall_50_96, shortfall_50_97, shortfall_50_98, shortfall_50_99, shortfall_100_0, shortfall_100_1, shortfall_100_2, shortfall_100_3, shortfall_100_4, shortfall_100_5, shortfall_100_6, shortfall_100_7, shortfall_100_8, shortfall_100_9, shortfall_200_0, shortfall_200_1, shortfall_200_2, shortfall_200_3, shortfall_200_4, shortfall_200_5, shortfall_200_6, shortfall_200_7, shortfall_200_8, shortfall_200_9
Contributor: Michał Adamaszek
    HMCR-n20-m400, HMCR-n20-m800, HMCR-n20-m1200, HMCR-n20-m1600, HMCR-n20-m2000, HMCR-n100-m400, HMCR-n100-m800, HMCR-n100-m1200, HMCR-n100-m1600, HMCR-n100-m2000, HMCR-n500-m400, HMCR-n500-m800, HMCR-n500-m1200, HMCR-n500-m1600, HMCR-n500-m2000
Contributor: Michał Adamaszek
    LogExpCR-n20-m400, LogExpCR-n20-m800, LogExpCR-n20-m1200, LogExpCR-n20-m1600, LogExpCR-n20-m2000, LogExpCR-n100-m400, LogExpCR-n100-m800, LogExpCR-n100-m1200, LogExpCR-n100-m1600, LogExpCR-n100-m2000, LogExpCR-n500-m400, LogExpCR-n500-m800, LogExpCR-n500-m1200, LogExpCR-n500-m1600, LogExpCR-n500-m2000
Contributor: Chris Coey
    port_12_9_3_a_1, port_12_9_3_a_2, port_12_9_3_b_1, port_12_9_3_b_2, port_12_9_3_c_1, port_12_9_3_c_2, port_12_9_3_d_1, port_12_9_3_d_2, port_16_12_4_a_1, port_16_12_4_a_2, port_16_12_4_b_1, port_16_12_4_b_2, port_16_12_4_c_1, port_16_12_4_c_2, port_16_12_4_d_1, port_16_12_4_d_2, port_20_15_5_a_1, port_20_15_5_a_2, port_20_15_5_b_1, port_20_15_5_b_2, port_20_15_5_c_1, port_20_15_5_c_2, port_20_15_5_d_1, port_20_15_5_d_2

Classical portfolio optimization with extras.

a) One cardinality constraint and
  * nothing extra (prefix: classical) [Vielma2008].
  * shortfall risk constraints (prefix: shortfall) [Vielma2008].
  * robust handling of uncertainty in expected returns (prefix: robust) [Vielma2008].

b) Instead of minimizing the standard deviation on return, these instances capture that only "negative" deviations are bad using downside risk measures:
  * higher moment coherent risk (prefix: HMCR) [Krokhmal2007]
  * log-exponential convex risk (prefix: LogExpCR) [Vinel2017]

c) Combinatorial stock selection constraints and multiple entropy, norm and robust norm risk constraints (prefix: port) [Coey2018].


@article{Vielma2008,
author = {Vielma, Juan Pablo and Ahmed, Shabbir and Nemhauser, George L.},
journal = {INFORMS Journal on Computing},
pages = {438--450},
title = {{A Lifted Linear Programming Branch-and-Bound Algorithm for Mixed Integer Conic Quadratic Programs}},
volume = {20},
year = {2008}
}

@article{Krokhmal2007,
author = {Krokhmal, Pavlo A.},
isbn = {1469768070},
issn = {14697688},
journal = {Quantitative Finance},
number = {4},
pages = {373--387},
title = {{Higher moment coherent risk measures}},
volume = {7},
year = {2007}
}

@article{Vinel2017,
author = {Vinel, Alexander and Krokhmal, Pavlo A.},
doi = {10.1007/s10479-015-1801-0},
issn = {15729338},
journal = {Annals of Operations Research},
number = {1-2},
pages = {75--95},
title = {{Certainty equivalent measures of risk}},
volume = {249},
year = {2017}
}

@article{Coey2018,
author = {Coey, Chris and Lubin, Miles and Vielma, Juan Pablo},
eprint = {1808.05290},
pages = {1--47},
title = {{Outer Approximation With Conic Certificates For Mixed-Integer Convex Problems}},
url = {http://arxiv.org/abs/1808.05290},
year = {2018}
}


=============================================================================
Production planning
=============================================================================
Contributor: Erling D. Andersen
    pp-n10-d10, pp-n10-d10000, pp-n100-d10, pp-n100-d10000, pp-n1000-d10, pp-n1000-d10000, pp-n100000-d10, pp-n100000-d10000

pages = {438--450},
title = {{A Lifted Linear Programming Branch-and-Bound Algorithm for Mixed Integer Conic Quadratic Programs}},
volume = {20},
year = {2008}
}


=============================================================================
Stochastic service system design
=============================================================================
Contributor: Henrik A. Friberg
    sssd-strong-15-4, sssd-strong-15-8, sssd-strong-20-4, sssd-strong-20-8, sssd-strong-25-4, sssd-strong-25-8, sssd-strong-30-4, sssd-strong-30-8, sssd-weak-15-4, sssd-weak-15-8, sssd-weak-20-4, sssd-weak-20-8, sssd-weak-25-4, sssd-weak-25-8, sssd-weak-30-4, sssd-weak-30-8

Stochastic service system design with M/M/1 queues using Strong formulation ('strong' in name), or weak formulation ('weak' in name).

@incollection{Linderoth2012,
author = {Linderoth, Jeff and G{\"{u}}nl{\"{u}}k, Oktay},
booktitle = {Mixed Integer Nonlinear Programming},
doi = {10.1007/978-1-4614-1927-3},
editor = {Lee, Jon and Leyffer, Sven},
isbn = {978-1-4614-1926-6},
pages = {61--89},
publisher = {Springer New York},
series = {The IMA Volumes in Mathematics and its Applications},
title = {{Perspective Reformulation And Applications}},
volume = {154},
year = {2012}
}

@article{Elhedhli2006,
author = {Elhedhli, Samir},
doi = {10.1287/msom.1050.0094},
issn = {1523-4614},
journal = {Manufacturing \& Service Operations Management},
month = jan,
number = {1},
pages = {92--97},
title = {{Service System Design with Immobile Servers, Stochastic Demand, and Congestion}},
volume = {8},
year = {2006}
}


=============================================================================
Topology optimization of 2D structures
=============================================================================
Contributor: Jakob Schelbert
    2D-TopOpt-Cantilever_60x40_50, 2D-TopOpt-MBB_60x40_50, 2D-TopOpt-Zhou-Rozvany_75

@article{Stolpe2010,
author = {Stolpe, Mathias and Bends\o e, Martin P.},
journal = {Structural and Multidisciplinary Optimization},
month = oct,
number = {2},
pages = {151--164},
title = {{Global optima for the Zhou–Rozvany problem}},
volume = {43},
year = {2010}
}

@book{Bendsoe2004,
author = {Bendsoe, Martin Philip and Sigmund, Ole},
publisher = {Springer},
series = {Engineering Online Library},
title = {{Topology Optimization}},
year = {2004}
}


=============================================================================
Topology optimization of truss structures
=============================================================================
Contributor: Jakob Schelbert
    achtziger_stolpe06-6.1flowc, achtziger_stolpe06-6.2flowc, achtziger_stolpe06-6.5bflowc, achtziger_stolpe06-6.5flowc, achtziger_stolpe07-5.1flowc, achtziger_stolpe07-5.2bflowc, achtziger_stolpe07-5.2flowc, achtziger_stolpe07-5.3flowc, b1bigflowc, stolpe07-8.1flowc, stolpe07-8.2flowc, stolpe07-8.3flowc

@article{Achtziger2006,
author = {Achtziger, Wolfgang and Stolpe, Mathias},
journal = {Structural and Multidisciplinary Optimization},
number = {1},
pages = {1--20},
title = {{Truss topology optimization with discrete design variables—Guaranteed global optimality and benchmark examples}},
volume = {34},
year = {2006}
}

@article{Achtziger2007,
author = {Achtziger, Wolfgang and Stolpe, Mathias},
journal = {Computational Optimization and Applications},
number = {2},
pages = {315--341},
title = {{Global optimization of truss topology with discrete bar areas—Part II: Implementation and numerical results}},
volume = {44},
year = {2007}
}

@article{Stolpe2007,
author = {Stolpe, M.},
journal = {Optimization and Engineering},
number = {2},
pages = {163--192},
title = {{On the reformulation of topology optimization problems as linear or convex quadratic mixed 0–1 programs}},
volume = {8},
year = {2007}
}


=============================================================================
Topology optimization of truss structures (robust)
=============================================================================
Contributor: Tristan Gally
    rt_2x3_3bars, rt_2x4_16bars, rt_2x4_2scen_3bars, rt_2x4_2scen_6bars, rt_2x4_3bars, rt_2x4_3bars_nominal, rt_2x4_8bars_2scen, rt_2x5_1scen_12bars, rt_2x5_1scen_3bars_nominal, rt_2x5_1scen_6bars, rt_2x5_1scen_8bars, rt_2x5_2scen_3bars, rt_2x5_2scen_4bars, rt_2x5_3bars, rt_2x6_3bars, rt_2x7_3bars, rt_3x3_2bars_3scen, rt_3x3_2fixed_8bars, rt_3x3_2scen_6bars, rt_3x3_2scen_8bars, rt_3x3_2scen_small_rob, rt_3x3_3scen_6bars, rt_3x3_3scen_8bars, rt_3x3_3scen, rt_3x3_5bars_2scen, rt_3x4_1scen_4bars, rt_3x4_1scen_6bars, rt_3x4_1scen_8bars, rt_3x4_2fixed_4bars_nominal, rt_4x3_2bars_3scen, rt_4x4_1bar_2scen, rt_4x4_1bar, rt_4x5_2bars, rt_5x5_1bar, rt_bridge_2x10_2bars_2scen, rt_bridge_2x5_5bars, rt_bridge_2x6_4bars_2scen, rt_bridge_2x7_4bars, rt_bridge_2x8_2bars_2scen, rt_bridge_2x8_2bars_2scen_nominal, rt_bridge_2x9_2bars, rt_bridge_2x9_2bars_nominal, rt_bridge_3x5_4bars, rt_bridge_3x5_4bars_nominal, rt_bridge_3x6_2bars_2scen, rt_bridge_3x7_2bars, rt_bridge_3x7_2bars_nominal, rt_bridge_3x8_1bar_2scen, rt_bridge_3x9_2bars, rt_demonst_1bar_3scen, rt_demonst_2bars_2scen, rt_demonstsmall_1bar_4scen, rt_demonstsmall_2bar_2scen_nominal, rt_demonstsmall_2bar_3scen, rt_demonstsmall_2bar_3scen_nominal, rt_demonstsmall_2bars_2scen, rt_demonstsmall_3bar_2scen_nominal, rt_demonstsmall_5bar_1scen_nominal, rt_test_bridge2, rt_test_bridge3

@unpublished{Gally2016,
author = {Gally, Tristan and Pfetsch, Marc E and Ulbrich, Stefan},
pages = {1--38},
title = {{A Framework for Solving Mixed-Integer Semidefinite Programs}}
}


=============================================================================
Balancing high-speed rotating machinery
=============================================================================
Contributor: Henrik A. Friberg
    turbine07, turbine07GF, turbine07_aniso, turbine07_lowb, turbine07_lowb_aniso, turbine54, turbine54GF

Balancing high-speed rotating machinery with either the least axial weight locations, the least distinct weight sets ('GF' in name), or minimum imbalance ('lowb' in name).

@phdthesis{Drewes2009,
author = {Drewes, Sarah},
number = {April 2009},
pages = {1--203},
school = {Technical University Darmstadt},
title = {{Mixed Integer Second Order Cone Programming}},
year = {2009}
}


=============================================================================
Facility location
=============================================================================
Contributor: Henrik A. Friberg
    uflquad-nopsc-10-100, uflquad-nopsc-10-150, uflquad-nopsc-20-100, uflquad-nopsc-20-150, uflquad-nopsc-30-100, uflquad-nopsc-30-150, uflquad-nopsc-30-200, uflquad-nopsc-30-300, uflquad-psc-10-100, uflquad-psc-10-150, uflquad-psc-20-100, uflquad-psc-20-150, uflquad-psc-30-100, uflquad-psc-30-150, uflquad-psc-30-200, uflquad-psc-30-300

Separable quadratic uncapacitated facility location (SQUFL). With cuts ('psc' in name) or without cuts ('nopsc' in name).

@incollection{Linderoth2012,
author = {Linderoth, Jeff and G{\"{u}}nl{\"{u}}k, Oktay},
booktitle = {Mixed Integer Nonlinear Programming},
doi = {10.1007/978-1-4614-1927-3},
editor = {Lee, Jon and Leyffer, Sven},
isbn = {978-1-4614-1926-6},
pages = {61--89},
publisher = {Springer New York},
series = {The IMA Volumes in Mathematics and its Applications},
title = {{Perspective Reformulation And Applications}},
volume = {154},
year = {2012}
}

@techreport{Gunluk2007,
author = {G\"{u}nl\"{u}k, Oktay and Lee, Jon and Weismantel, Robert},
institution = {IBM Research Report RC24213},
title = {{MINLP strengthening for separable convex quadratic transportation-cost UFL}},
volume = {24213},
year = {2007}
}


=============================================================================
Unit commitment
=============================================================================
Contributor: Linfeng Yang
    10_0_1_w, 10_0_2_w, 10_0_3_w, 10_0_4_w, 10_0_5_w, 10_std, 20_0_1_w, 20_0_2_w, 20_0_3_w, 20_0_4_w, 20_0_5_w, 50_0_1_w, 50_0_2_w, 50_0_3_w, 50_0_4_w, 50_0_5_w, 75_0_1_w, 75_0_2_w, 75_0_3_w, 75_0_4_w, 75_0_5_w, 100_0_1_w, 100_0_2_w, 100_0_3_w, 100_0_4_w, 100_0_5_w, 150_0_1_w, 150_0_2_w, 150_0_3_w, 150_0_4_w, 150_0_5_w, 200_0_1_w, 200_0_2_w, 200_0_3_w, 200_0_4_w, 200_0_5_w, 200_0_6_w, 200_0_7_w, 200_0_8_w, 200_0_9_w, 200_0_10_w, 200_0_11_w, 200_0_12_w

Pure-thermal unit commitment problems after perspective reformulation.

@article{Yang2015,
author = {Yang, Linfeng and Jian, Jinbao and Zhu, Yunan and Dong, Zhaoyang},
journal = {IEEE Transactions on Power Systems},
number = {1},
pages = {13--23},
title = {{Tight Relaxation Method for Unit Commitment Problem Using Reformulation and Lift-and-Project}},
volume = {30},
year = {2015}
}


=============================================================================
Cardinality constrained least squares
=============================================================================
Contributor: Tristan Gally
    coloncancer_1001_1100_6, coloncancer_101_200_7, coloncancer_1_100_5, coloncancer_1101_1200_8, coloncancer_1201_1300_10, coloncancer_1301_1400_12, coloncancer_1401_1500_14, coloncancer_1501_1600_16, coloncancer_1601_1700_18, coloncancer_1701_1800_20, coloncancer_1801_1900_22, coloncancer_1901_2000_24, coloncancer_201_300_9, coloncancer_301_400_11, coloncancer_401_500_13, coloncancer_501_600_15, coloncancer_601_700_17, coloncancer_701_800_19, coloncancer_801_900_21, coloncancer_901_1000_23
Contributor: Tristan Gally
    clsq_random_128_2_a, clsq_random_128_2_b, clsq_random_128_2_c, clsq_random_128_4_a, clsq_random_128_4_b, clsq_random_128_4_c, clsq_random_128_6_a, clsq_random_128_6_a, clsq_random_128_6_b, clsq_random_128_6_c, clsq_random_32_2_a, clsq_random_32_2_b, clsq_random_32_2_c, clsq_random_32_4_a, clsq_random_32_4_b, clsq_random_32_4_c, clsq_random_32_6_a, clsq_random_32_6_b, clsq_random_32_6_c, clsq_random_32_8_a, clsq_random_32_8_b, clsq_random_32_8_c, clsq_random_64_2_a, clsq_random_64_2_b, clsq_random_64_2_c, clsq_random_64_4_a, clsq_random_64_4_b, clsq_random_64_4_c, clsq_random_64_6_a, clsq_random_64_6_b, clsq_random_64_6_c, clsq_random_64_8_a, clsq_random_64_8_b, clsq_random_64_8_c, clsq_random_96_2_a, clsq_random_96_2_b, clsq_random_96_2_c, clsq_random_96_4_a, clsq_random_96_4_b, clsq_random_96_4_c, clsq_random_96_6_a, clsq_random_96_6_b, clsq_random_96_6_c, clsq_random_96_8_a, clsq_random_96_8_b, clsq_random_96_8_c

@unpublished{Gally2016,
author = {Gally, Tristan and Pfetsch, Marc E and Ulbrich, Stefan},
pages = {1--38},
title = {{A Framework for Solving Mixed-Integer Semidefinite Programs}}
}

@article{Pilanci2015,
author = {Mert Pilanci and Martin J. Wainwright and Laurent {El Ghaoui}},
journal = {Mathematical Programming Series B},
number = {1},
title = {Sparse learning via {B}oolean relaxations},
volume = {151},
pages = {62--87},
year = {2015},
}


=============================================================================
Minimum cost k-partitioning
=============================================================================
Contributor: Tristan Gally
    kpart_diw.15.4.29, kpart_diw.34.4.71, kpart_diw.37.4.92, kpart_diw.38.4.105, kpart_diw.42.4.132, kpart_diw.43.4.105, kpart_diw.44.4.105, kpart_diw.46.4.79, kpart_diw.48.4.81, kpart_ven.17.4.39
Contributor: Tristan Gally
    kpart_2g_4_164_k3_5_6, kpart_2g_5_25_k3_8_9, kpart_2g_6_701_k10_3_4, kpart_2g_6_701_k18_2_2, kpart_2g_6_701_k2_18_18, kpart_2g_6_701_k3_12_12, kpart_2g_6_701_k4_9_9, kpart_2g_6_701_k5_7_8, kpart_2g_6_701_k6_6_6, kpart_2g_6_701_k7_5_6, kpart_2g_6_701_k8_4_5, kpart_2g_6_701_k9_4_4, kpart_2g_7_77_k3_16_17, kpart_2pm_5_55_k10_2_3, kpart_2pm_5_55_k2_12_13, kpart_2pm_5_55_k3_8_9, kpart_2pm_5_55_k4_6_7, kpart_2pm_5_55_k5_5_5, kpart_2pm_5_55_k6_4_5, kpart_2pm_5_55_k7_3_4, kpart_2pm_5_55_k8_3_4, kpart_2pm_5_55_k9_2_3, kpart_3g_244_244_k10_3_4, kpart_3g_244_244_k16_2_2, kpart_3g_244_244_k2_16_16, kpart_3g_244_244_k3_10_11, kpart_3g_244_244_k4_8_8, kpart_3g_244_244_k5_6_7, kpart_3g_244_244_k6_5_6, kpart_3g_244_244_k7_4_5, kpart_3g_244_244_k8_4_4, kpart_3g_244_244_k9_3_4, kpart_3pm_234_234_k10_2_3, kpart_3pm_234_234_k12_2_2, kpart_3pm_234_234_k2_12_12, kpart_3pm_234_234_k3_8_8, kpart_3pm_234_234_k4_6_6, kpart_3pm_234_234_k5_5_6, kpart_3pm_234_234_k6_4_4, kpart_3pm_234_234_k7_3_4, kpart_3pm_234_234_k8_3_3, kpart_3pm_234_234_k9_2_3, kpart_clique_20_k3_6_7, kpart_clique_30_k3_10_10, kpart_clique_40_k3_13_14, kpart_clique_50_k3_16_17, kpart_clique_60_k10_6_6, kpart_clique_60_k15_4_4, kpart_clique_60_k20_3_3, kpart_clique_60_k2_30_30, kpart_clique_60_k30_2_2, kpart_clique_60_k3_20_20, kpart_clique_60_k4_15_15, kpart_clique_60_k5_12_12, kpart_clique_60_k6_10_10, kpart_clique_60_k7_8_9, kpart_clique_60_k8_7_8, kpart_clique_60_k9_6_7, kpart_clique_70_k3_23_24

The real-world instances ('kpart_diw' and 'kpart_ven' prefix) from [Ferreira2011] treat the clustering of cells on a chip as a preprocessing step for the layout-problem in very-large-scale integration (VLSI).

@unpublished{Gally2016,
author = {Gally, Tristan and Pfetsch, Marc E and Ulbrich, Stefan},
pages = {1--38},
title = {{A Framework for Solving Mixed-Integer Semidefinite Programs}}
}

@misc{Ferreira2011,
 author = {C. E. Ferreira, A. Martin, C. C. de Souza, R. Weismantel and L. A. Wolsey},
 title = {The Node Capacitated Graph Partitioning Problem -- Benchmark Instances},
 year = {2011},
 note = {\sl{www.ic.unicamp.br/$\sim$cid/Problem-instances/Graph-Partition}}
}


=============================================================================
Assortment optimization under the mixed multinomial logit model
=============================================================================
Contributor: Alper Atamtürk
    as_conic_100_100_hard_set1_1_cap100, as_conic_100_100_hard_set1_1_cap10, as_conic_100_100_hard_set1_1_cap20, as_conic_100_100_hard_set1_1_cap50, as_conic_100_100_hard_set1_2_cap100, as_conic_100_100_hard_set1_2_cap10, as_conic_100_100_hard_set1_2_cap20, as_conic_100_100_hard_set1_2_cap50, as_conic_100_100_hard_set2_1_cap100, as_conic_100_100_hard_set2_1_cap10, as_conic_100_100_hard_set2_1_cap20, as_conic_100_100_hard_set2_1_cap50, as_conic_100_100_hard_set2_2_cap100, as_conic_100_100_hard_set2_2_cap10, as_conic_100_100_hard_set2_2_cap20, as_conic_100_100_hard_set2_2_cap50, as_conic_100_100_hard_set3_1_cap100, as_conic_100_100_hard_set3_1_cap10, as_conic_100_100_hard_set3_1_cap20, as_conic_100_100_hard_set3_1_cap50, as_conic_100_100_hard_set3_2_cap100, as_conic_100_100_hard_set3_2_cap10, as_conic_100_100_hard_set3_2_cap20, as_conic_100_100_hard_set3_2_cap50, as_conic_100_100_hard_set4_1_cap100, as_conic_100_100_hard_set4_1_cap10, as_conic_100_100_hard_set4_1_cap20, as_conic_100_100_hard_set4_1_cap50, as_conic_100_100_hard_set4_2_cap100, as_conic_100_100_hard_set4_2_cap10, as_conic_100_100_hard_set4_2_cap20, as_conic_100_100_hard_set4_2_cap50, as_conic_100_100_hard_set5_1_cap100, as_conic_100_100_hard_set5_1_cap10, as_conic_100_100_hard_set5_1_cap20, as_conic_100_100_hard_set5_1_cap50, as_conic_100_100_hard_set5_2_cap100, as_conic_100_100_hard_set5_2_cap10, as_conic_100_100_hard_set5_2_cap20, as_conic_100_100_hard_set5_2_cap50, as_conic_200_20_set1_10_cap100, as_conic_200_20_set1_10_cap10, as_conic_200_20_set1_10_cap200, as_conic_200_20_set1_10_cap20, as_conic_200_20_set1_10_cap50, as_conic_200_20_set1_5_cap100, as_conic_200_20_set1_5_cap10, as_conic_200_20_set1_5_cap200, as_conic_200_20_set1_5_cap20, as_conic_200_20_set1_5_cap50, as_conic_200_20_set2_10_cap100, as_conic_200_20_set2_10_cap10, as_conic_200_20_set2_10_cap200, as_conic_200_20_set2_10_cap20, as_conic_200_20_set2_10_cap50, as_conic_200_20_set2_5_cap100, as_conic_200_20_set2_5_cap10, as_conic_200_20_set2_5_cap200, as_conic_200_20_set2_5_cap20, as_conic_200_20_set2_5_cap50, as_conic_200_20_set3_10_cap100, as_conic_200_20_set3_10_cap10, as_conic_200_20_set3_10_cap200, as_conic_200_20_set3_10_cap20, as_conic_200_20_set3_10_cap50, as_conic_200_20_set3_5_cap100, as_conic_200_20_set3_5_cap10, as_conic_200_20_set3_5_cap200, as_conic_200_20_set3_5_cap20, as_conic_200_20_set3_5_cap50, as_conic_200_20_set4_10_cap100, as_conic_200_20_set4_10_cap10, as_conic_200_20_set4_10_cap200, as_conic_200_20_set4_10_cap20, as_conic_200_20_set4_10_cap50, as_conic_200_20_set4_5_cap100, as_conic_200_20_set4_5_cap10, as_conic_200_20_set4_5_cap200, as_conic_200_20_set4_5_cap20, as_conic_200_20_set4_5_cap50, as_conic_200_20_set5_10_cap100, as_conic_200_20_set5_10_cap10, as_conic_200_20_set5_10_cap200, as_conic_200_20_set5_10_cap20, as_conic_200_20_set5_10_cap50, as_conic_200_20_set5_5_cap100, as_conic_200_20_set5_5_cap10, as_conic_200_20_set5_5_cap200, as_conic_200_20_set5_5_cap20, as_conic_200_20_set5_5_cap50, as_conic_500_50_set1_10_cap100, as_conic_500_50_set1_10_cap200, as_conic_500_50_set1_10_cap20, as_conic_500_50_set1_10_cap500, as_conic_500_50_set1_10_cap50, as_conic_500_50_set1_20_cap100, as_conic_500_50_set1_20_cap200, as_conic_500_50_set1_20_cap20, as_conic_500_50_set1_20_cap500, as_conic_500_50_set1_20_cap50, as_conic_500_50_set2_10_cap100, as_conic_500_50_set2_10_cap200, as_conic_500_50_set2_10_cap20, as_conic_500_50_set2_10_cap500, as_conic_500_50_set2_10_cap50, as_conic_500_50_set2_20_cap100, as_conic_500_50_set2_20_cap200, as_conic_500_50_set2_20_cap20, as_conic_500_50_set2_20_cap500, as_conic_500_50_set2_20_cap50, as_conic_500_50_set3_10_cap100, as_conic_500_50_set3_10_cap200, as_conic_500_50_set3_10_cap20, as_conic_500_50_set3_10_cap500, as_conic_500_50_set3_10_cap50, as_conic_500_50_set3_20_cap100, as_conic_500_50_set3_20_cap200, as_conic_500_50_set3_20_cap20, as_conic_500_50_set3_20_cap500, as_conic_500_50_set3_20_cap50, as_conic_500_50_set4_10_cap100, as_conic_500_50_set4_10_cap200, as_conic_500_50_set4_10_cap20, as_conic_500_50_set4_10_cap500, as_conic_500_50_set4_10_cap50, as_conic_500_50_set4_20_cap100, as_conic_500_50_set4_20_cap200, as_conic_500_50_set4_20_cap20, as_conic_500_50_set4_20_cap500, as_conic_500_50_set4_20_cap50, as_conic_500_50_set5_10_cap100, as_conic_500_50_set5_10_cap200, as_conic_500_50_set5_10_cap20, as_conic_500_50_set5_10_cap500, as_conic_500_50_set5_10_cap50, as_conic_500_50_set5_20_cap100, as_conic_500_50_set5_20_cap200, as_conic_500_50_set5_20_cap20, as_conic_500_50_set5_20_cap500, as_conic_500_50_set5_20_cap50, as_conic_frozenpizza_2_cap100, as_conic_frozenpizza_2_cap10, as_conic_frozenpizza_2_cap20, as_conic_frozenpizza_2_cap222, as_conic_frozenpizza_2_cap50, as_conic_frozenpizza_5_cap100, as_conic_frozenpizza_5_cap10, as_conic_frozenpizza_5_cap20, as_conic_frozenpizza_5_cap222, as_conic_frozenpizza_5_cap50
Contributor: Alper Atamtürk
    as_conic_mc_100_100_hard_set1_1_cap100, as_conic_mc_100_100_hard_set1_1_cap10, as_conic_mc_100_100_hard_set1_1_cap20, as_conic_mc_100_100_hard_set1_1_cap50, as_conic_mc_100_100_hard_set1_2_cap100, as_conic_mc_100_100_hard_set1_2_cap10, as_conic_mc_100_100_hard_set1_2_cap20, as_conic_mc_100_100_hard_set1_2_cap50, as_conic_mc_100_100_hard_set2_1_cap100, as_conic_mc_100_100_hard_set2_1_cap10, as_conic_mc_100_100_hard_set2_1_cap20, as_conic_mc_100_100_hard_set2_1_cap50, as_conic_mc_100_100_hard_set2_2_cap100, as_conic_mc_100_100_hard_set2_2_cap10, as_conic_mc_100_100_hard_set2_2_cap20, as_conic_mc_100_100_hard_set2_2_cap50, as_conic_mc_100_100_hard_set3_1_cap100, as_conic_mc_100_100_hard_set3_1_cap10, as_conic_mc_100_100_hard_set3_1_cap20, as_conic_mc_100_100_hard_set3_1_cap50, as_conic_mc_100_100_hard_set3_2_cap100, as_conic_mc_100_100_hard_set3_2_cap10, as_conic_mc_100_100_hard_set3_2_cap20, as_conic_mc_100_100_hard_set3_2_cap50, as_conic_mc_100_100_hard_set4_1_cap100, as_conic_mc_100_100_hard_set4_1_cap10, as_conic_mc_100_100_hard_set4_1_cap20, as_conic_mc_100_100_hard_set4_1_cap50, as_conic_mc_100_100_hard_set4_2_cap100, as_conic_mc_100_100_hard_set4_2_cap10, as_conic_mc_100_100_hard_set4_2_cap20, as_conic_mc_100_100_hard_set4_2_cap50, as_conic_mc_100_100_hard_set5_1_cap100, as_conic_mc_100_100_hard_set5_1_cap10, as_conic_mc_100_100_hard_set5_1_cap20, as_conic_mc_100_100_hard_set5_1_cap50, as_conic_mc_100_100_hard_set5_2_cap100, as_conic_mc_100_100_hard_set5_2_cap10, as_conic_mc_100_100_hard_set5_2_cap20, as_conic_mc_100_100_hard_set5_2_cap50, as_conic_mc_200_20_set1_10_cap100, as_conic_mc_200_20_set1_10_cap10, as_conic_mc_200_20_set1_10_cap200, as_conic_mc_200_20_set1_10_cap20, as_conic_mc_200_20_set1_10_cap50, as_conic_mc_200_20_set1_5_cap100, as_conic_mc_200_20_set1_5_cap10, as_conic_mc_200_20_set1_5_cap200, as_conic_mc_200_20_set1_5_cap20, as_conic_mc_200_20_set1_5_cap50, as_conic_mc_200_20_set2_10_cap100, as_conic_mc_200_20_set2_10_cap10, as_conic_mc_200_20_set2_10_cap200, as_conic_mc_200_20_set2_10_cap20, as_conic_mc_200_20_set2_10_cap50, as_conic_mc_200_20_set2_5_cap100, as_conic_mc_200_20_set2_5_cap10, as_conic_mc_200_20_set2_5_cap200, as_conic_mc_200_20_set2_5_cap20, as_conic_mc_200_20_set2_5_cap50, as_conic_mc_200_20_set3_10_cap100, as_conic_mc_200_20_set3_10_cap10, as_conic_mc_200_20_set3_10_cap200, as_conic_mc_200_20_set3_10_cap20, as_conic_mc_200_20_set3_10_cap50, as_conic_mc_200_20_set3_5_cap100, as_conic_mc_200_20_set3_5_cap10, as_conic_mc_200_20_set3_5_cap200, as_conic_mc_200_20_set3_5_cap20, as_conic_mc_200_20_set3_5_cap50, as_conic_mc_200_20_set4_10_cap100, as_conic_mc_200_20_set4_10_cap10, as_conic_mc_200_20_set4_10_cap200, as_conic_mc_200_20_set4_10_cap20, as_conic_mc_200_20_set4_10_cap50, as_conic_mc_200_20_set4_5_cap100, as_conic_mc_200_20_set4_5_cap10, as_conic_mc_200_20_set4_5_cap200, as_conic_mc_200_20_set4_5_cap20, as_conic_mc_200_20_set4_5_cap50, as_conic_mc_200_20_set5_10_cap100, as_conic_mc_200_20_set5_10_cap10, as_conic_mc_200_20_set5_10_cap200, as_conic_mc_200_20_set5_10_cap20, as_conic_mc_200_20_set5_10_cap50, as_conic_mc_200_20_set5_5_cap100, as_conic_mc_200_20_set5_5_cap10, as_conic_mc_200_20_set5_5_cap200, as_conic_mc_200_20_set5_5_cap20, as_conic_mc_200_20_set5_5_cap50, as_conic_mc_500_50_set1_10_cap100, as_conic_mc_500_50_set1_10_cap200, as_conic_mc_500_50_set1_10_cap20, as_conic_mc_500_50_set1_10_cap500, as_conic_mc_500_50_set1_10_cap50, as_conic_mc_500_50_set1_20_cap100, as_conic_mc_500_50_set1_20_cap200, as_conic_mc_500_50_set1_20_cap20, as_conic_mc_500_50_set1_20_cap500, as_conic_mc_500_50_set1_20_cap50, as_conic_mc_500_50_set2_10_cap100, as_conic_mc_500_50_set2_10_cap200, as_conic_mc_500_50_set2_10_cap20, as_conic_mc_500_50_set2_10_cap500, as_conic_mc_500_50_set2_10_cap50, as_conic_mc_500_50_set2_20_cap100, as_conic_mc_500_50_set2_20_cap200, as_conic_mc_500_50_set2_20_cap20, as_conic_mc_500_50_set2_20_cap500, as_conic_mc_500_50_set2_20_cap50, as_conic_mc_500_50_set3_10_cap100, as_conic_mc_500_50_set3_10_cap200, as_conic_mc_500_50_set3_10_cap20, as_conic_mc_500_50_set3_10_cap500, as_conic_mc_500_50_set3_10_cap50, as_conic_mc_500_50_set3_20_cap100, as_conic_mc_500_50_set3_20_cap200, as_conic_mc_500_50_set3_20_cap20, as_conic_mc_500_50_set3_20_cap500, as_conic_mc_500_50_set3_20_cap50, as_conic_mc_500_50_set4_10_cap100, as_conic_mc_500_50_set4_10_cap200, as_conic_mc_500_50_set4_10_cap20, as_conic_mc_500_50_set4_10_cap500, as_conic_mc_500_50_set4_10_cap50, as_conic_mc_500_50_set4_20_cap100, as_conic_mc_500_50_set4_20_cap200, as_conic_mc_500_50_set4_20_cap20, as_conic_mc_500_50_set4_20_cap500, as_conic_mc_500_50_set4_20_cap50, as_conic_mc_500_50_set5_10_cap100, as_conic_mc_500_50_set5_10_cap200, as_conic_mc_500_50_set5_10_cap20, as_conic_mc_500_50_set5_10_cap500, as_conic_mc_500_50_set5_10_cap50, as_conic_mc_500_50_set5_20_cap100, as_conic_mc_500_50_set5_20_cap200, as_conic_mc_500_50_set5_20_cap20, as_conic_mc_500_50_set5_20_cap500, as_conic_mc_500_50_set5_20_cap50, as_conic_mc_frozenpizza_2_cap100, as_conic_mc_frozenpizza_2_cap10, as_conic_mc_frozenpizza_2_cap20, as_conic_mc_frozenpizza_2_cap222, as_conic_mc_frozenpizza_2_cap50, as_conic_mc_frozenpizza_5_cap100, as_conic_mc_frozenpizza_5_cap10, as_conic_mc_frozenpizza_5_cap20, as_conic_mc_frozenpizza_5_cap222, as_conic_mc_frozenpizza_5_cap50

Formulations with the 'as_conic_mc' prefix are strengthed by McCormick inequalities.

@techreport{Sen2015,
author = {Şen, Alper and Atamturk, Alper and Kaminsky, Philop},
title = {{A Conic Integer Programming Approach to Constrained Assortment Optimization under the Mixed Multinomial Logit Model}},
institution = {University of California, Berkeley},
type = {BCOL RESEARCH REPORT 15.06},
year = {2015}
}


=============================================================================
Infeasible SDP instances (academic)
=============================================================================
Contributor: Gabor Pataki
    infeas_clean_10_10_1, infeas_clean_10_10_2, infeas_clean_10_10_3, infeas_clean_10_10_4, infeas_clean_10_10_5, infeas_clean_10_10_6, infeas_clean_10_10_7, infeas_clean_10_10_8, infeas_clean_10_10_9, infeas_clean_10_10_10, infeas_clean_10_10_11, infeas_clean_10_10_12, infeas_clean_10_10_13, infeas_clean_10_10_14, infeas_clean_10_10_15, infeas_clean_10_10_16, infeas_clean_10_10_17, infeas_clean_10_10_18, infeas_clean_10_10_19, infeas_clean_10_10_20, infeas_clean_10_10_21, infeas_clean_10_10_22, infeas_clean_10_10_23, infeas_clean_10_10_24, infeas_clean_10_10_25, infeas_clean_10_10_26, infeas_clean_10_10_27, infeas_clean_10_10_28, infeas_clean_10_10_29, infeas_clean_10_10_30, infeas_clean_10_10_31, infeas_clean_10_10_32, infeas_clean_10_10_33, infeas_clean_10_10_34, infeas_clean_10_10_35, infeas_clean_10_10_36, infeas_clean_10_10_37, infeas_clean_10_10_38, infeas_clean_10_10_39, infeas_clean_10_10_40, infeas_clean_10_10_41, infeas_clean_10_10_42, infeas_clean_10_10_43, infeas_clean_10_10_44, infeas_clean_10_10_45, infeas_clean_10_10_46, infeas_clean_10_10_47, infeas_clean_10_10_48, infeas_clean_10_10_49, infeas_clean_10_10_50, infeas_clean_10_10_51, infeas_clean_10_10_52, infeas_clean_10_10_53, infeas_clean_10_10_54, infeas_clean_10_10_55, infeas_clean_10_10_56, infeas_clean_10_10_57, infeas_clean_10_10_58, infeas_clean_10_10_59, infeas_clean_10_10_60, infeas_clean_10_10_61, infeas_clean_10_10_62, infeas_clean_10_10_63, infeas_clean_10_10_64, infeas_clean_10_10_65, infeas_clean_10_10_66, infeas_clean_10_10_67, infeas_clean_10_10_68, infeas_clean_10_10_69, infeas_clean_10_10_70, infeas_clean_10_10_71, infeas_clean_10_10_72, infeas_clean_10_10_73, infeas_clean_10_10_74, infeas_clean_10_10_75, infeas_clean_10_10_76, infeas_clean_10_10_77, infeas_clean_10_10_78, infeas_clean_10_10_79, infeas_clean_10_10_80, infeas_clean_10_10_81, infeas_clean_10_10_82, infeas_clean_10_10_83, infeas_clean_10_10_84, infeas_clean_10_10_85, infeas_clean_10_10_86, infeas_clean_10_10_87, infeas_clean_10_10_88, infeas_clean_10_10_89, infeas_clean_10_10_90, infeas_clean_10_10_91, infeas_clean_10_10_92, infeas_clean_10_10_93, infeas_clean_10_10_94, infeas_clean_10_10_95, infeas_clean_10_10_96, infeas_clean_10_10_97, infeas_clean_10_10_98, infeas_clean_10_10_99, infeas_clean_10_10_100, infeas_clean_20_10_1, infeas_clean_20_10_2, infeas_clean_20_10_3, infeas_clean_20_10_4, infeas_clean_20_10_5, infeas_clean_20_10_6, infeas_clean_20_10_7, infeas_clean_20_10_8, infeas_clean_20_10_9, infeas_clean_20_10_10, infeas_clean_20_10_11, infeas_clean_20_10_12, infeas_clean_20_10_13, infeas_clean_20_10_14, infeas_clean_20_10_15, infeas_clean_20_10_16, infeas_clean_20_10_17, infeas_clean_20_10_18, infeas_clean_20_10_19, infeas_clean_20_10_20, infeas_clean_20_10_21, infeas_clean_20_10_22, infeas_clean_20_10_23, infeas_clean_20_10_24, infeas_clean_20_10_25, infeas_clean_20_10_26, infeas_clean_20_10_27, infeas_clean_20_10_28, infeas_clean_20_10_29, infeas_clean_20_10_30, infeas_clean_20_10_31, infeas_clean_20_10_32, infeas_clean_20_10_33, infeas_clean_20_10_34, infeas_clean_20_10_35, infeas_clean_20_10_36, infeas_clean_20_10_37, infeas_clean_20_10_38, infeas_clean_20_10_39, infeas_clean_20_10_40, infeas_clean_20_10_41, infeas_clean_20_10_42, infeas_clean_20_10_43, infeas_clean_20_10_44, infeas_clean_20_10_45, infeas_clean_20_10_46, infeas_clean_20_10_47, infeas_clean_20_10_48, infeas_clean_20_10_49, infeas_clean_20_10_50, infeas_clean_20_10_51, infeas_clean_20_10_52, infeas_clean_20_10_53, infeas_clean_20_10_54, infeas_clean_20_10_55, infeas_clean_20_10_56, infeas_clean_20_10_57, infeas_clean_20_10_58, infeas_clean_20_10_59, infeas_clean_20_10_60, infeas_clean_20_10_61, infeas_clean_20_10_62, infeas_clean_20_10_63, infeas_clean_20_10_64, infeas_clean_20_10_65, infeas_clean_20_10_66, infeas_clean_20_10_67, infeas_clean_20_10_68, infeas_clean_20_10_69, infeas_clean_20_10_70, infeas_clean_20_10_71, infeas_clean_20_10_72, infeas_clean_20_10_73, infeas_clean_20_10_74, infeas_clean_20_10_75, infeas_clean_20_10_76, infeas_clean_20_10_77, infeas_clean_20_10_78, infeas_clean_20_10_79, infeas_clean_20_10_80, infeas_clean_20_10_81, infeas_clean_20_10_82, infeas_clean_20_10_83, infeas_clean_20_10_84, infeas_clean_20_10_85, infeas_clean_20_10_86, infeas_clean_20_10_87, infeas_clean_20_10_88, infeas_clean_20_10_89, infeas_clean_20_10_90, infeas_clean_20_10_91, infeas_clean_20_10_92, infeas_clean_20_10_93, infeas_clean_20_10_94, infeas_clean_20_10_95, infeas_clean_20_10_96, infeas_clean_20_10_97, infeas_clean_20_10_98, infeas_clean_20_10_99, infeas_clean_20_10_100, infeas_messy_10_10_1, infeas_messy_10_10_2, infeas_messy_10_10_3, infeas_messy_10_10_4, infeas_messy_10_10_5, infeas_messy_10_10_6, infeas_messy_10_10_7, infeas_messy_10_10_8, infeas_messy_10_10_9, infeas_messy_10_10_10, infeas_messy_10_10_11, infeas_messy_10_10_12, infeas_messy_10_10_13, infeas_messy_10_10_14, infeas_messy_10_10_15, infeas_messy_10_10_16, infeas_messy_10_10_17, infeas_messy_10_10_18, infeas_messy_10_10_19, infeas_messy_10_10_20, infeas_messy_10_10_21, infeas_messy_10_10_22, infeas_messy_10_10_23, infeas_messy_10_10_24, infeas_messy_10_10_25, infeas_messy_10_10_26, infeas_messy_10_10_27, infeas_messy_10_10_28, infeas_messy_10_10_29, infeas_messy_10_10_30, infeas_messy_10_10_31, infeas_messy_10_10_32, infeas_messy_10_10_33, infeas_messy_10_10_34, infeas_messy_10_10_35, infeas_messy_10_10_36, infeas_messy_10_10_37, infeas_messy_10_10_38, infeas_messy_10_10_39, infeas_messy_10_10_40, infeas_messy_10_10_41, infeas_messy_10_10_42, infeas_messy_10_10_43, infeas_messy_10_10_44, infeas_messy_10_10_45, infeas_messy_10_10_46, infeas_messy_10_10_47, infeas_messy_10_10_48, infeas_messy_10_10_49, infeas_messy_10_10_50, infeas_messy_10_10_51, infeas_messy_10_10_52, infeas_messy_10_10_53, infeas_messy_10_10_54, infeas_messy_10_10_55, infeas_messy_10_10_56, infeas_messy_10_10_57, infeas_messy_10_10_58, infeas_messy_10_10_59, infeas_messy_10_10_60, infeas_messy_10_10_61, infeas_messy_10_10_62, infeas_messy_10_10_63, infeas_messy_10_10_64, infeas_messy_10_10_65, infeas_messy_10_10_66, infeas_messy_10_10_67, infeas_messy_10_10_68, infeas_messy_10_10_69, infeas_messy_10_10_70, infeas_messy_10_10_71, infeas_messy_10_10_72, infeas_messy_10_10_73, infeas_messy_10_10_74, infeas_messy_10_10_75, infeas_messy_10_10_76, infeas_messy_10_10_77, infeas_messy_10_10_78, infeas_messy_10_10_79, infeas_messy_10_10_80, infeas_messy_10_10_81, infeas_messy_10_10_82, infeas_messy_10_10_83, infeas_messy_10_10_84, infeas_messy_10_10_85, infeas_messy_10_10_86, infeas_messy_10_10_87, infeas_messy_10_10_88, infeas_messy_10_10_89, infeas_messy_10_10_90, infeas_messy_10_10_91, infeas_messy_10_10_92, infeas_messy_10_10_93, infeas_messy_10_10_94, infeas_messy_10_10_95, infeas_messy_10_10_96, infeas_messy_10_10_97, infeas_messy_10_10_98, infeas_messy_10_10_99, infeas_messy_10_10_100, infeas_messy_20_10_1, infeas_messy_20_10_2, infeas_messy_20_10_3, infeas_messy_20_10_4, infeas_messy_20_10_5, infeas_messy_20_10_6, infeas_messy_20_10_7, infeas_messy_20_10_8, infeas_messy_20_10_9, infeas_messy_20_10_10, infeas_messy_20_10_11, infeas_messy_20_10_12, infeas_messy_20_10_13, infeas_messy_20_10_14, infeas_messy_20_10_15, infeas_messy_20_10_16, infeas_messy_20_10_17, infeas_messy_20_10_18, infeas_messy_20_10_19, infeas_messy_20_10_20, infeas_messy_20_10_21, infeas_messy_20_10_22, infeas_messy_20_10_23, infeas_messy_20_10_24, infeas_messy_20_10_25, infeas_messy_20_10_26, infeas_messy_20_10_27, infeas_messy_20_10_28, infeas_messy_20_10_29, infeas_messy_20_10_30, infeas_messy_20_10_31, infeas_messy_20_10_32, infeas_messy_20_10_33, infeas_messy_20_10_34, infeas_messy_20_10_35, infeas_messy_20_10_36, infeas_messy_20_10_37, infeas_messy_20_10_38, infeas_messy_20_10_39, infeas_messy_20_10_40, infeas_messy_20_10_41, infeas_messy_20_10_42, infeas_messy_20_10_43, infeas_messy_20_10_44, infeas_messy_20_10_45, infeas_messy_20_10_46, infeas_messy_20_10_47, infeas_messy_20_10_48, infeas_messy_20_10_49, infeas_messy_20_10_50, infeas_messy_20_10_51, infeas_messy_20_10_52, infeas_messy_20_10_53, infeas_messy_20_10_54, infeas_messy_20_10_55, infeas_messy_20_10_56, infeas_messy_20_10_57, infeas_messy_20_10_58, infeas_messy_20_10_59, infeas_messy_20_10_60, infeas_messy_20_10_61, infeas_messy_20_10_62, infeas_messy_20_10_63, infeas_messy_20_10_64, infeas_messy_20_10_65, infeas_messy_20_10_66, infeas_messy_20_10_67, infeas_messy_20_10_68, infeas_messy_20_10_69, infeas_messy_20_10_70, infeas_messy_20_10_71, infeas_messy_20_10_72, infeas_messy_20_10_73, infeas_messy_20_10_74, infeas_messy_20_10_75, infeas_messy_20_10_76, infeas_messy_20_10_77, infeas_messy_20_10_78, infeas_messy_20_10_79, infeas_messy_20_10_80, infeas_messy_20_10_81, infeas_messy_20_10_82, infeas_messy_20_10_83, infeas_messy_20_10_84, infeas_messy_20_10_85, infeas_messy_20_10_86, infeas_messy_20_10_87, infeas_messy_20_10_88, infeas_messy_20_10_89, infeas_messy_20_10_90, infeas_messy_20_10_91, infeas_messy_20_10_92, infeas_messy_20_10_93, infeas_messy_20_10_94, infeas_messy_20_10_95, infeas_messy_20_10_96, infeas_messy_20_10_97, infeas_messy_20_10_98, infeas_messy_20_10_99, infeas_messy_20_10_100, 
Contributor: Gabor Pataki
    weak_clean_10_10_1, weak_clean_10_10_2, weak_clean_10_10_3, weak_clean_10_10_4, weak_clean_10_10_5, weak_clean_10_10_6, weak_clean_10_10_7, weak_clean_10_10_8, weak_clean_10_10_9, weak_clean_10_10_10, weak_clean_10_10_11, weak_clean_10_10_12, weak_clean_10_10_13, weak_clean_10_10_14, weak_clean_10_10_15, weak_clean_10_10_16, weak_clean_10_10_17, weak_clean_10_10_18, weak_clean_10_10_19, weak_clean_10_10_20, weak_clean_10_10_21, weak_clean_10_10_22, weak_clean_10_10_23, weak_clean_10_10_24, weak_clean_10_10_25, weak_clean_10_10_26, weak_clean_10_10_27, weak_clean_10_10_28, weak_clean_10_10_29, weak_clean_10_10_30, weak_clean_10_10_31, weak_clean_10_10_32, weak_clean_10_10_33, weak_clean_10_10_34, weak_clean_10_10_35, weak_clean_10_10_36, weak_clean_10_10_37, weak_clean_10_10_38, weak_clean_10_10_39, weak_clean_10_10_40, weak_clean_10_10_41, weak_clean_10_10_42, weak_clean_10_10_43, weak_clean_10_10_44, weak_clean_10_10_45, weak_clean_10_10_46, weak_clean_10_10_47, weak_clean_10_10_48, weak_clean_10_10_49, weak_clean_10_10_50, weak_clean_10_10_51, weak_clean_10_10_52, weak_clean_10_10_53, weak_clean_10_10_54, weak_clean_10_10_55, weak_clean_10_10_56, weak_clean_10_10_57, weak_clean_10_10_58, weak_clean_10_10_59, weak_clean_10_10_60, weak_clean_10_10_61, weak_clean_10_10_62, weak_clean_10_10_63, weak_clean_10_10_64, weak_clean_10_10_65, weak_clean_10_10_66, weak_clean_10_10_67, weak_clean_10_10_68, weak_clean_10_10_69, weak_clean_10_10_70, weak_clean_10_10_71, weak_clean_10_10_72, weak_clean_10_10_73, weak_clean_10_10_74, weak_clean_10_10_75, weak_clean_10_10_76, weak_clean_10_10_77, weak_clean_10_10_78, weak_clean_10_10_79, weak_clean_10_10_80, weak_clean_10_10_81, weak_clean_10_10_82, weak_clean_10_10_83, weak_clean_10_10_84, weak_clean_10_10_85, weak_clean_10_10_86, weak_clean_10_10_87, weak_clean_10_10_88, weak_clean_10_10_89, weak_clean_10_10_90, weak_clean_10_10_91, weak_clean_10_10_92, weak_clean_10_10_93, weak_clean_10_10_94, weak_clean_10_10_95, weak_clean_10_10_96, weak_clean_10_10_97, weak_clean_10_10_98, weak_clean_10_10_99, weak_clean_10_10_100, weak_clean_20_10_1, weak_clean_20_10_2, weak_clean_20_10_3, weak_clean_20_10_4, weak_clean_20_10_5, weak_clean_20_10_6, weak_clean_20_10_7, weak_clean_20_10_8, weak_clean_20_10_9, weak_clean_20_10_10, weak_clean_20_10_11, weak_clean_20_10_12, weak_clean_20_10_13, weak_clean_20_10_14, weak_clean_20_10_15, weak_clean_20_10_16, weak_clean_20_10_17, weak_clean_20_10_18, weak_clean_20_10_19, weak_clean_20_10_20, weak_clean_20_10_21, weak_clean_20_10_22, weak_clean_20_10_23, weak_clean_20_10_24, weak_clean_20_10_25, weak_clean_20_10_26, weak_clean_20_10_27, weak_clean_20_10_28, weak_clean_20_10_29, weak_clean_20_10_30, weak_clean_20_10_31, weak_clean_20_10_32, weak_clean_20_10_33, weak_clean_20_10_34, weak_clean_20_10_35, weak_clean_20_10_36, weak_clean_20_10_37, weak_clean_20_10_38, weak_clean_20_10_39, weak_clean_20_10_40, weak_clean_20_10_41, weak_clean_20_10_42, weak_clean_20_10_43, weak_clean_20_10_44, weak_clean_20_10_45, weak_clean_20_10_46, weak_clean_20_10_47, weak_clean_20_10_48, weak_clean_20_10_49, weak_clean_20_10_50, weak_clean_20_10_51, weak_clean_20_10_52, weak_clean_20_10_53, weak_clean_20_10_54, weak_clean_20_10_55, weak_clean_20_10_56, weak_clean_20_10_57, weak_clean_20_10_58, weak_clean_20_10_59, weak_clean_20_10_60, weak_clean_20_10_61, weak_clean_20_10_62, weak_clean_20_10_63, weak_clean_20_10_64, weak_clean_20_10_65, weak_clean_20_10_66, weak_clean_20_10_67, weak_clean_20_10_68, weak_clean_20_10_69, weak_clean_20_10_70, weak_clean_20_10_71, weak_clean_20_10_72, weak_clean_20_10_73, weak_clean_20_10_74, weak_clean_20_10_75, weak_clean_20_10_76, weak_clean_20_10_77, weak_clean_20_10_78, weak_clean_20_10_79, weak_clean_20_10_80, weak_clean_20_10_81, weak_clean_20_10_82, weak_clean_20_10_83, weak_clean_20_10_84, weak_clean_20_10_85, weak_clean_20_10_86, weak_clean_20_10_87, weak_clean_20_10_88, weak_clean_20_10_89, weak_clean_20_10_90, weak_clean_20_10_91, weak_clean_20_10_92, weak_clean_20_10_93, weak_clean_20_10_94, weak_clean_20_10_95, weak_clean_20_10_96, weak_clean_20_10_97, weak_clean_20_10_98, weak_clean_20_10_99, weak_clean_20_10_100, weak_messy_10_10_1, weak_messy_10_10_2, weak_messy_10_10_3, weak_messy_10_10_4, weak_messy_10_10_5, weak_messy_10_10_6, weak_messy_10_10_7, weak_messy_10_10_8, weak_messy_10_10_9, weak_messy_10_10_10, weak_messy_10_10_11, weak_messy_10_10_12, weak_messy_10_10_13, weak_messy_10_10_14, weak_messy_10_10_15, weak_messy_10_10_16, weak_messy_10_10_17, weak_messy_10_10_18, weak_messy_10_10_19, weak_messy_10_10_20, weak_messy_10_10_21, weak_messy_10_10_22, weak_messy_10_10_23, weak_messy_10_10_24, weak_messy_10_10_25, weak_messy_10_10_26, weak_messy_10_10_27, weak_messy_10_10_28, weak_messy_10_10_29, weak_messy_10_10_30, weak_messy_10_10_31, weak_messy_10_10_32, weak_messy_10_10_33, weak_messy_10_10_34, weak_messy_10_10_35, weak_messy_10_10_36, weak_messy_10_10_37, weak_messy_10_10_38, weak_messy_10_10_39, weak_messy_10_10_40, weak_messy_10_10_41, weak_messy_10_10_42, weak_messy_10_10_43, weak_messy_10_10_44, weak_messy_10_10_45, weak_messy_10_10_46, weak_messy_10_10_47, weak_messy_10_10_48, weak_messy_10_10_49, weak_messy_10_10_50, weak_messy_10_10_51, weak_messy_10_10_52, weak_messy_10_10_53, weak_messy_10_10_54, weak_messy_10_10_55, weak_messy_10_10_56, weak_messy_10_10_57, weak_messy_10_10_58, weak_messy_10_10_59, weak_messy_10_10_60, weak_messy_10_10_61, weak_messy_10_10_62, weak_messy_10_10_63, weak_messy_10_10_64, weak_messy_10_10_65, weak_messy_10_10_66, weak_messy_10_10_67, weak_messy_10_10_68, weak_messy_10_10_69, weak_messy_10_10_70, weak_messy_10_10_71, weak_messy_10_10_72, weak_messy_10_10_73, weak_messy_10_10_74, weak_messy_10_10_75, weak_messy_10_10_76, weak_messy_10_10_77, weak_messy_10_10_78, weak_messy_10_10_79, weak_messy_10_10_80, weak_messy_10_10_81, weak_messy_10_10_82, weak_messy_10_10_83, weak_messy_10_10_84, weak_messy_10_10_85, weak_messy_10_10_86, weak_messy_10_10_87, weak_messy_10_10_88, weak_messy_10_10_89, weak_messy_10_10_90, weak_messy_10_10_91, weak_messy_10_10_92, weak_messy_10_10_93, weak_messy_10_10_94, weak_messy_10_10_95, weak_messy_10_10_96, weak_messy_10_10_97, weak_messy_10_10_98, weak_messy_10_10_99, weak_messy_10_10_100, weak_messy_20_10_1, weak_messy_20_10_2, weak_messy_20_10_3, weak_messy_20_10_4, weak_messy_20_10_5, weak_messy_20_10_6, weak_messy_20_10_7, weak_messy_20_10_8, weak_messy_20_10_9, weak_messy_20_10_10, weak_messy_20_10_11, weak_messy_20_10_12, weak_messy_20_10_13, weak_messy_20_10_14, weak_messy_20_10_15, weak_messy_20_10_16, weak_messy_20_10_17, weak_messy_20_10_18, weak_messy_20_10_19, weak_messy_20_10_20, weak_messy_20_10_21, weak_messy_20_10_22, weak_messy_20_10_23, weak_messy_20_10_24, weak_messy_20_10_25, weak_messy_20_10_26, weak_messy_20_10_27, weak_messy_20_10_28, weak_messy_20_10_29, weak_messy_20_10_30, weak_messy_20_10_31, weak_messy_20_10_32, weak_messy_20_10_33, weak_messy_20_10_34, weak_messy_20_10_35, weak_messy_20_10_36, weak_messy_20_10_37, weak_messy_20_10_38, weak_messy_20_10_39, weak_messy_20_10_40, weak_messy_20_10_41, weak_messy_20_10_42, weak_messy_20_10_43, weak_messy_20_10_44, weak_messy_20_10_45, weak_messy_20_10_46, weak_messy_20_10_47, weak_messy_20_10_48, weak_messy_20_10_49, weak_messy_20_10_50, weak_messy_20_10_51, weak_messy_20_10_52, weak_messy_20_10_53, weak_messy_20_10_54, weak_messy_20_10_55, weak_messy_20_10_56, weak_messy_20_10_57, weak_messy_20_10_58, weak_messy_20_10_59, weak_messy_20_10_60, weak_messy_20_10_61, weak_messy_20_10_62, weak_messy_20_10_63, weak_messy_20_10_64, weak_messy_20_10_65, weak_messy_20_10_66, weak_messy_20_10_67, weak_messy_20_10_68, weak_messy_20_10_69, weak_messy_20_10_70, weak_messy_20_10_71, weak_messy_20_10_72, weak_messy_20_10_73, weak_messy_20_10_74, weak_messy_20_10_75, weak_messy_20_10_76, weak_messy_20_10_77, weak_messy_20_10_78, weak_messy_20_10_79, weak_messy_20_10_80, weak_messy_20_10_81, weak_messy_20_10_82, weak_messy_20_10_83, weak_messy_20_10_84, weak_messy_20_10_85, weak_messy_20_10_86, weak_messy_20_10_87, weak_messy_20_10_88, weak_messy_20_10_89, weak_messy_20_10_90, weak_messy_20_10_91, weak_messy_20_10_92, weak_messy_20_10_93, weak_messy_20_10_94, weak_messy_20_10_95, weak_messy_20_10_96, weak_messy_20_10_97, weak_messy_20_10_98, weak_messy_20_10_99, weak_messy_20_10_100

Formulations with the 'infeas' prefix are generated from the class of all infeasible instances. Formulations with the 'weak' prefix are generated from the subclass of all weakly infeasible instances, that is, the subclass in which there are no dual improving rays (Farkas certificates of infeasibility) and the distance to feasibility is zero. All instances are stored in integer arithmetic, and have [Liu2015a]-type certificates of infeasibility in integer arithmetic, to guarantee preservation of the numerically fragile weakly infeasible states. In the 'clean' instances these [Liu2015a]-type certificates are easy to find, and in the 'messy' instances they are hard to find.

@article{Liu2015a,
author = {Liu, Minghui and Pataki, Gabor},
journal = {Mathematical Programming (to appear)},
title = {{Exact duals and short certificates of infeasibility and weak infeasibility in conic linear programming}},
year = {2015},
note = {\sl{http://gaborpataki.web.unc.edu/infeasible-and-weakly-infeasible-sdps/}}
}


=============================================================================
Experiment design problem
=============================================================================
Contributor: Chris Coey
    expdesign_A_12_6, expdesign_A_16_8, expdesign_A_20_10, expdesign_A_24_12, expdesign_A_28_14, expdesign_A_32_16, expdesign_A_8_4, expdesign_D_12_6, expdesign_D_16_8, expdesign_D_20_10, expdesign_D_24_12, expdesign_D_28_14, expdesign_D_32_16, expdesign_D_8_4, expdesign_E_12_6, expdesign_E_16_8, expdesign_E_20_10, expdesign_E_24_12, expdesign_E_28_14, expdesign_E_32_16, expdesign_E_8_4

Design of experiments (vectors a[i]) such that 'x' can be determined with highest accuracy from noisy measuresments of the type 'y[i] = a[i]^T x + w[i]', where w[i] is independent Gaussian noise.

@book{Boyd2004,
author = {Boyd, Stephen and Vandenberghe, Lieven},
title = {{Convex Optimization}},
isbn = {9780521833783},
publisher = {Cambridge University Press},
url = {https://web.stanford.edu/{~}boyd/cvxbook/bv{\_}cvxbook.pdf},
year = {2004}
}

@article{Coey2018,
author = {Coey, Chris and Lubin, Miles and Vielma, Juan Pablo},
eprint = {1808.05290},
pages = {1--47},
title = {{Outer Approximation With Conic Certificates For Mixed-Integer Convex Problems}},
url = {http://arxiv.org/abs/1808.05290},
year = {2018}
}


=============================================================================
Steam turbine design
=============================================================================
Contributor: Kenneth O. Kortanek
    fiac81b

@techreport{Fiacco1981,
address = {Washington D.C.},
author = {Fiacco, Anthony V. and Ghaemi, Abolfazl},
pages = {1--36},
series = {T-437},
title = {{Sensitivity and Parametric Bound Analysis of an Electric Power Generation GP Model: Optimal Steam Turbine Exhaust Annulus and Condenser Sizes}},
year = {1981}
}

@article{Andersen1998,
author = {Andersen, Erling D. and Ye, Yinyu},
journal = {Computational Optimization and Applications},
number = {3},
pages = {243--269},
title = {{A Computational Study of the Homogeneous Algorithm for Large-scale Convex Optimization}},
volume = {10},
year = {1998}
}


=============================================================================
Water polution control
=============================================================================
Contributor: Kenneth O. Kortanek
    fiac81a

@article{Fiacco1982,
author = {Fiacco, Anthony V. and Ghaemi, Abolfazl},
journal = {Operations Research},
number = {1},
title = {{Sensitivity Analysis of a Nonlinear Water Pollution Control Model Using an Upper Hudson River Data Base}},
volume = {30},
year = {1982}
}

@article{Andersen1998,
author = {Andersen, Erling D. and Ye, Yinyu},
journal = {Computational Optimization and Applications},
number = {3},
pages = {243--269},
title = {{A Computational Study of the Homogeneous Algorithm for Large-scale Convex Optimization}},
volume = {10},
year = {1998}
}


=============================================================================
Inventory management
=============================================================================
Contributor: Kenneth O. Kortanek
    jha88

@unpublished{Jha1988,
address = {Iowa City},
author = {Jha, S. and Kortanek, K.O. and No, H.},
institution = {College of Business Administration, The University of Iowa},
series = {88-12},
title = {{Lotsizing and setup time reduction under stochastic demand: A geometric programming approach}},
year = {1988}
}

@article{Andersen1998,
author = {Andersen, Erling D. and Ye, Yinyu},
journal = {Computational Optimization and Applications},
number = {3},
pages = {243--269},
title = {{A Computational Study of the Homogeneous Algorithm for Large-scale Convex Optimization}},
volume = {10},
year = {1998}
}


=============================================================================
Geometric programming instances (unknown purpose)
=============================================================================
Contributor: Kenneth O. Kortanek
    beck751, beck752, beck753, bss1, bss2, car, cx02-100, cx02-200, demb761, demb762, demb763, demb781, demb782, fang88, gp_dave_1, gp_dave_2, gp_dave_3, gptest, isil01, mra01, mra02, rijc781, rijc782, rijc783, rijc784, rijc785, rijc786, rijc787, varun

@article{Andersen1998,
author = {Andersen, Erling D. and Ye, Yinyu},
journal = {Computational Optimization and Applications},
number = {3},
pages = {243--269},
title = {{A Computational Study of the Homogeneous Algorithm for Large-scale Convex Optimization}},
volume = {10},
year = {1998}
}


=============================================================================
Batch processing
=============================================================================
Contributor: Miles Lubin
    batch, batchdes
Contributor: Miles Lubin
    enpro48, enpro56, ravem

Converted from MINLPLib (http://www.minlplib.org/).

The original source:
'batch','batchdes'              [Kocis1988].
'enpro48','enpro56','ravem'     Aldo Vecchietti's Model Collection.

@article{Kocis1988,
author = {Kocis, Gary R. and Grossmann, Ignacio E.},
doi = {10.1021/ie00080a013},
journal = {Industrial and Engineering Chemistry Research},
number = {8},
pages = {1407--1421},
title = {{Global Optimization of Nonconvex Mixed-Integer Nonlinear Programming (Minlp) Problems in Process Synthesis}},
volume = {27},
year = {1988}
}


=============================================================================
Batch Plant Design
=============================================================================
Contributor: Miles Lubin
    batchs101006m, batchs121208m, batchs151208m, batchs201210m

Determine volume of equipment, number of units to operate in parallel, and locations of intermediate storage tanks.

Converted from MINLPLib (http://www.minlplib.org/).

@article{Ravemark1998,
author = {Ravemark, Dag E. and Rippin, David W.T.},
doi = {10.1016/S0098-1354(96)00357-2},
journal = {Computers {\&} Chemical Engineering},
number = {1-2},
pages = {177--183},
title = {{Optimal design of a multi-product batch plant}},
volume = {22},
year = {1998}
}

@inproceedings{Vecchietti1999,
author = {Vecchietti, Aldo and Grossmann, Ignacio E.},
booktitle = {Computers and Chemical Engineering},
doi = {10.1016/S0098-1354(98)00293-2},
mendeley-groups = {CBLIB},
number = {4-5},
pages = {555--565},
title = {{LOGMIP: A disjunctive 0-1 non-linear optimizer for process system models}},
volume = {23},
year = {1999}
}


=============================================================================
MINLPLib instances (unknown purpose)
=============================================================================
Contributor: Miles Lubin
    gams01, netmod_dol2, netmod_kar1, netmod_kar2, du-opt, du-opt5, nvs03, ex1223, ex1223a, ex1223b, gbd

Converted from MINLPLib (http://www.minlplib.org/).

The original source:
'gams01','netmod*'      Unknown GAMS Clients.
'du-opt*'               Bram Schoonen's Model Collection.
'nvs03'                 See [Gupta1985].
'ex1223*'               Reformulations of [Yuan1988, Problème P1]. See also [Floudas2014, §12.2.3].
'gbd'                   See [Floudas1995, page 133].

@article{Gupta1985,
author = {Gupta, Omprakash K. and Ravindran, A.},
doi = {10.1287/mnsc.31.12.1533},
journal = {Management Science},
number = {12},
pages = {1533--1546},
title = {{Branch and Bound Experiments in Convex Nonlinear Integer Programming}},
volume = {31},
year = {1985}
}

@article{Yuan1988,
author = {Yuan, X. and Zhang, S. and Pibouleau, L. and Domenech, S.},
journal = {RAIRO - Operations Research},
number = {4},
title = {{Une m{\'{e}}thode d'optimisation non lin{\'{e}}aire en variables mixtes pour la conception de proc{\'{e}}d{\'{e}}s}},
volume = {22},
year = {1988}
}

@book{Floudas1995,
author = {Floudas, Christodoulos A.},
publisher = {Oxford University Press},
title = {{Nonlinear and Mixed-Integer Optimization - Fundamentals and Applications}},
year = {1995}
}

@book{Floudas2014,
author = {Floudas, Christodoulos A. and Pardalos, P{\~{a}}nos M. and Adjiman, Claire S. and Esposito, William R. and G{\"{u}}m{\"{u}}s, Zeynep H. and Harding, Stephen T. and Klepeis, John L. and Meyer, Clifford A. and Schweiger, Carl A.},
doi = {10.1007/s13398-014-0173-7.2},
publisher = {Kluwer Academic Publishers},
title = {{Handbook of test problems in local and global optimization}},
year = {1999}
}


=============================================================================
Maximizing attractiveness of a new product in multiattribute space
=============================================================================
Contributor: Miles Lubin
    ex4

Converted from MINLPLib (http://www.minlplib.org/).

@article{Duran1986,
author = {Duran, Marco A. and Grossmann, Ignacio E.},
journal = {Mathematical Programming},
number = {3},
pages = {307--339},
title = {{An outer-approximation algorithm for a class of mixed-integer nonlinear programs}},
volume = {36},
year = {1986}
}


=============================================================================
Multi-commodity facility location/allocation
=============================================================================
Contributor: Miles Lubin
    fac3

Converted from MINLPLib (http://www.minlplib.org/).


=============================================================================
Constrained layout problem
=============================================================================
Contributor: Miles Lubin
    clay0203h, clay0203m, clay0204h, clay0204m, clay0205h, clay0205m, clay0303h, clay0303m, clay0304h, clay0304m, clay0305h, clay0305m

Non-overlapping rectangular units must be placed within the confines of certain designated circular areas such that the cost of connecting these units is minimized.

Big-M formulations ('m' postfix) and convex hull formulations ('h' postfix) [Sawaya2007,Bonami2008]. Big-M formulations tigthened in [Goez2013b].

Converted from MINLPLib (http://www.minlplib.org/). Tiny epsilon constants used for avoiding division by zero have been removed since these are not needed in the conic formulation.

Originally available through the CMU-IBM MINLP solver project page: http://egon.cheme.cmu.edu/ibm/page.htm.

@article{Sawaya2007,
abstract = {Lee and Grossmann [Lee, S., {\&} Grossmann, I. E. (2000). New algorithms for nonlinear generalized disjunctive programming. Computers and Chemical Engineering, 24, 2125-2141] have developed a reformulation for nonlinear Generalized Disjunctive Programming (GDP) p$
author = {Sawaya, Nicolas W. and Grossmann, Ignacio E.},
doi = {10.1016/j.compchemeng.2006.08.002},
journal = {Computers and Chemical Engineering},
number = {7},
pages = {856--866},
title = {{Computational implementation of non-linear convex hull reformulation}},
volume = {31},
year = {2007}
}

@article{Bonami2008,
author = {Bonami, Pierre and Biegler, Lorenz T. and Conn, Andrew R. and Cornu{\'{e}}jols, G{\'{e}}rard and Grossmann, Ignacio E. and Laird, Carl D. and Lee, Jon and Lodi, Andrea and Margot, Fran{\c{c}}ois and Sawaya, Nicolas and W{\"{a}}chter, Andreas},
doi = {10.1016/j.disopt.2006.10.011},
journal = {Discrete Optimization},
month = {may},
number = {2},
pages = {186--204},
title = {{An algorithmic framework for convex mixed integer nonlinear programs}},
volume = {5},
year = {2008}
}

@phdthesis{Goez2013b,
author = {G{\'{o}}ez, Julio Cesar},
school = {Lehigh University},
title = {{Mixed Integer Second Order Cone Optimization Disjunctive Conic Cuts: Theory and experiments}},
url = {http://phd.ie.lehigh.edu/~jgoez/wp-content/uploads/thesisJCGoez.pdf},
year = {2013}
}

@incollection{Lubin2016,
author = {Lubin, Miles and Yamangil, Emre and Bent, Russel and Vielma, Juan Pablo},
booktitle = {Integer Programming and Combinatorial Optimization: 18th International Conference},
pages = {102--113},
publisher = {Springer International Publishing},
title = {{Extended Formulations in Mixed-integer Convex Programming}},
year = {2016}
}


=============================================================================
Farm land layout
=============================================================================
Contributor: Miles Lubin
    flay02h, flay02m, flay03h, flay03m, flay04h, flay04m, flay05h, flay05m, flay06h, flay06m

Determines the optimal length and width of a number of rectangular patches of land with fixed area, such that the perimeter of the set of patches is minimized.

Big-M formulations ('m' postfix) and convex hull formulations ('h' postfix) [Sawaya2007,Bonami2008]. 

Converted from MINLPLib (http://www.minlplib.org/).

Originally available through the CMU-IBM MINLP solver project page: http://egon.cheme.cmu.edu/ibm/page.htm.

@article{Sawaya2007,
abstract = {Lee and Grossmann [Lee, S., {\&} Grossmann, I. E. (2000). New algorithms for nonlinear generalized disjunctive programming. Computers and Chemical Engineering, 24, 2125-2141] have developed a reformulation for nonlinear Generalized Disjunctive Programming (GDP) p$
author = {Sawaya, Nicolas W. and Grossmann, Ignacio E.},
doi = {10.1016/j.compchemeng.2006.08.002},
journal = {Computers and Chemical Engineering},
number = {7},
pages = {856--866},
title = {{Computational implementation of non-linear convex hull reformulation}},
volume = {31},
year = {2007}
}

@article{Bonami2008,
author = {Bonami, Pierre and Biegler, Lorenz T. and Conn, Andrew R. and Cornu{\'{e}}jols, G{\'{e}}rard and Grossmann, Ignacio E. and Laird, Carl D. and Lee, Jon and Lodi, Andrea and Margot, Fran{\c{c}}ois and Sawaya, Nicolas and W{\"{a}}chter, Andreas},
doi = {10.1016/j.disopt.2006.10.011},
journal = {Discrete Optimization},
month = {may},
number = {2},
pages = {186--204},
title = {{An algorithmic framework for convex mixed integer nonlinear programs}},
volume = {5},
year = {2008}
}


=============================================================================
Safety layout problem
=============================================================================
Contributor: Miles Lubin
    slay04h, slay04m, slay05h, slay05m, slay06h, slay06m, slay07h, slay07m, slay08h, slay08m, slay09h, slay09m, slay10h, slay10m

Determine the optimal placement of a set of units with fixed width and length such that the Euclidean distance between their center point and a predefined "safety point" is minimized.

Big-M formulations ('m' postfix) and convex hull formulations ('h' postfix) [Sawaya2007,Bonami2008]. 

Converted from MINLPLib (http://www.minlplib.org/).

Originally available through the CMU-IBM MINLP solver project page: http://egon.cheme.cmu.edu/ibm/page.htm.

@article{Sawaya2007,
abstract = {Lee and Grossmann [Lee, S., {\&} Grossmann, I. E. (2000). New algorithms for nonlinear generalized disjunctive programming. Computers and Chemical Engineering, 24, 2125-2141] have developed a reformulation for nonlinear Generalized Disjunctive Programming (GDP) p$
author = {Sawaya, Nicolas W. and Grossmann, Ignacio E.},
doi = {10.1016/j.compchemeng.2006.08.002},
journal = {Computers and Chemical Engineering},
number = {7},
pages = {856--866},
title = {{Computational implementation of non-linear convex hull reformulation}},
volume = {31},
year = {2007}
}

@article{Bonami2008,
author = {Bonami, Pierre and Biegler, Lorenz T. and Conn, Andrew R. and Cornu{\'{e}}jols, G{\'{e}}rard and Grossmann, Ignacio E. and Laird, Carl D. and Lee, Jon and Lodi, Andrea and Margot, Fran{\c{c}}ois and Sawaya, Nicolas and W{\"{a}}chter, Andreas},
doi = {10.1016/j.disopt.2006.10.011},
journal = {Discrete Optimization},
month = {may},
number = {2},
pages = {186--204},
title = {{An algorithmic framework for convex mixed integer nonlinear programs}},
volume = {5},
year = {2008}
}


=============================================================================
Facility layout problem
=============================================================================
Contributor: Miles Lubin
    fo7, fo7_2, fo8, fo9, m3, m6, m7, o7, o7_2
Contributor: Miles Lubin
    fo7_ar2_1, fo7_ar25_1, fo7_ar3_1, fo7_ar4_1, fo7_ar5_1, fo8_ar2_1, fo8_ar25_1, fo8_ar3_1, fo8_ar4_1, fo8_ar5_1, fo9_ar2_1, fo9_ar25_1, fo9_ar3_1, fo9_ar4_1, fo9_ar5_1, m7_ar2_1, m7_ar25_1, m7_ar3_1, m7_ar4_1, m7_ar5_1, no7_ar2_1, no7_ar25_1, no7_ar3_1, no7_ar4_1, no7_ar5_1, o7_ar2_1, o7_ar25_1, o7_ar3_1, o7_ar4_1, o7_ar5_1, o8_ar4_1, o9_ar4_1

Without aspect ratio constraints [Meller1998].
With aspect ratio constraints (marked with 'ar') [Castillo2005].

Converted from MINLPLib (http://www.minlplib.org/).

@article{Meller1998,
author = {Meller, Russell D. and Narayanan, Venkat and Vance, Pamela H.},
doi = {10.1016/S0167-6377(98)00024-8},
journal = {Operations Research Letters},
number = {3-5},
pages = {117--127},
title = {{Optimal facility layout design}},
volume = {23},
year = {1998}
}

@article{Castillo2005,
author = {Castillo, Ignacio and Westerlund, Joakim and Emet, Stefan and Westerlund, Tapio},
doi = {10.1016/j.compchemeng.2005.07.012},
journal = {Computers and Chemical Engineering},
number = {1},
pages = {54--69},
title = {{Optimization of block layout design problems with unequal areas: A comparison of MILP and MINLP optimization methods}},
volume = {30},
year = {2005}
}


=============================================================================
Social Accounting Matrix Balancing
=============================================================================
Contributor: Miles Lubin
    sambal

@article{Drud1989,
author = {Drud, Arne and Zenios, Stavros A. and Mulvey, John M.},
doi = {10.1002/net.3230190507},
journal = {Networks},
number = {5},
pages = {569--585},
title = {{Balancing large social accounting matrices with nonlinear network programming}},
volume = {19},
year = {1989}
}


=============================================================================
Outlier detection in time series
=============================================================================
Contributor: Andrés Gómez
    wiener_strong_signal100-3-101, wiener_strong_signal100-3-102, wiener_strong_signal100-3-103, wiener_strong_signal100-3-104, wiener_strong_signal100-3-105, wiener_strong_signal200-3-101, wiener_strong_signal200-3-102, wiener_strong_signal200-3-103, wiener_strong_signal200-3-104, wiener_strong_signal200-3-105, wiener_strong_signal200-15-101, wiener_strong_signal200-15-102, wiener_strong_signal200-15-103, wiener_strong_signal200-15-104, wiener_strong_signal200-15-105, wiener_strong_signal200-u-101, wiener_strong_signal200-u-102, wiener_strong_signal200-u-103, wiener_strong_signal200-u-104, wiener_strong_signal200-u-105, wiener_strong_signal500-3-101, wiener_strong_signal500-3-102, wiener_strong_signal500-3-103, wiener_strong_signal500-3-104, wiener_strong_signal500-3-105
Contributor: Andrés Gómez
    wiener_weak_signal100-3-101, wiener_weak_signal100-3-102, wiener_weak_signal100-3-103, wiener_weak_signal100-3-104, wiener_weak_signal100-3-105, wiener_weak_signal200-3-101, wiener_weak_signal200-3-102, wiener_weak_signal200-3-103, wiener_weak_signal200-3-104, wiener_weak_signal200-3-105, wiener_weak_signal200-15-101, wiener_weak_signal200-15-102, wiener_weak_signal200-15-103, wiener_weak_signal200-15-104, wiener_weak_signal200-15-105, wiener_weak_signal200-u-101, wiener_weak_signal200-u-102, wiener_weak_signal200-u-103, wiener_weak_signal200-u-104, wiener_weak_signal200-u-105, wiener_weak_signal500-3-101, wiener_weak_signal500-3-102, wiener_weak_signal500-3-103, wiener_weak_signal500-3-104, wiener_weak_signal500-3-105

Solves the MAP estimation problem for observations of a noisy Wiener process mixed with outliers from an independent source.
This corresponds to a Trimmed Least Squares estimation with ridge regularization on smoothness
With convex hull strengthening ('strong' in name) and without ('weak' in name) [Gomez2019].

@techreport{Gomez2019,
author = {G{\'{o}}mez, Andr{\'{e}}s},
institution = {Department of Industrial and Systems Engineering, University of Southern California},
title = {{Outlier detection in time series via mixed-integer conic quadratic optimization}},
year = {2019}
}