test_problems.jl

# Copyright (c) 2014: SCS.jl contributors
#
# Use of this source code is governed by an MIT-style license that can be found
# in the LICENSE.md file or at https://opensource.org/licenses/MIT.

# We don't want this file formatted because it contains lots of data.
#! format: off

using SparseArrays

# Random, feasible conic problem (no exponential or SDP cones)
# Problem data taken from https://github.com/cvxgrp/scs/blob/master/examples/raw/demo_data
function feasible_basic_conic(T)
    n = 11
    m = 112

    z = 0
    l = 100
    bu = Float64[]
    bl = Float64[]
    ep = 0
    ed = 0
    q = [12]
    s = Int[]

    b = [
    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0,
    0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5]

    c = [
    -3.687045235791087894, -3.143065318037093103,  2.289111017442006180,
     1.817854752120232842,  1.515395029041534336, -0.621501577833336483,
     0.896216656096898490, -0.946791055758523736,  1.016669273321816114,
    -0.487162812452204275,  1.000000000000000000]

    colptr = [0, 101, 202, 303, 404, 505, 606, 707, 808, 909, 1010, 1012]

    rowval = [
     0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
    19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
    38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
    57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
    76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,
    95, 96, 97, 98, 99, 101, 0,  1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
    15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
    34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
    53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
    72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
    91, 92, 93, 94, 95, 96, 97, 98, 99, 102, 0,  1,  2,  3,  4,  5,  6,  7,  8,
     9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
    28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
    47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,
    66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
    85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 103, 0,  1,  2,
     3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
    22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
    41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
    60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,
    79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
    98, 99, 104, 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,
    16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
    35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
    54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
    73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
    92, 93, 94, 95, 96, 97, 98, 99, 105, 0,  1,  2,  3,  4,  5,  6,  7,  8,  9,
    10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
    29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
    48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
    67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
    86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 106, 0,  1,  2,  3,
     4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
    23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
    42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
    61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
    80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,
    99, 107, 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
    17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
    36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
    55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
    74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
    93, 94, 95, 96, 97, 98, 99, 108, 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
    11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
    30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
    49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
    68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
    87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 109, 0,  1,  2,  3,  4,
     5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
    24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
    43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
    62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
    81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
    110, 100, 111]

    values = [
     0.267905121455780493, -0.267905121455780493,  1.027917777157112633,
    -1.027917777157112633, -2.297179456574058865,  2.297179456574058865,
    -0.236264446681208273,  0.236264446681208273, -1.867877520107915412,
     1.867877520107915412,  0.162954278135808150, -0.162954278135808150,
     0.235147192773076164, -0.235147192773076164,  0.683684798830484719,
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     1.416761181304521111,  0.065959446153038476, -0.065959446153038476,
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     1.956795979420658416,  1.136562859433311701, -1.136562859433311701,
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     0.794072741149785766,  1.371420904545398223, -1.371420904545398223,
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     0.178086679323349178, -0.178086679323349178, -0.726541129368893035,
     0.726541129368893035,  0.102548417306546763, -0.102548417306546763,
     0.101435234812034022, -0.101435234812034022, -0.607294919138270162,
     0.607294919138270162, -0.232543338993652815,  0.232543338993652815,
     1.024873011278554902, -1.024873011278554902, -0.367070741104161269,
     0.367070741104161269,  0.015584587754633259, -0.015584587754633259,
     0.893352765523915404, -0.893352765523915404, -0.318598602767036110,
     0.318598602767036110, -0.565309322249061386,  0.565309322249061386,
    -0.869520326873197580,  0.869520326873197580,  1.087601931698385149,
    -1.087601931698385149, -1.000000000000000000, -1.000000000000000000,
    -1.000000000000000000]

    A = SparseMatrixCSC(m, n, colptr .+ 1, rowval .+ 1, vec(values))
    P = spzeros(n, n)

    sol = scs_solve(T, m, n, A, P, b, c, z, l, bu, bl, q, s, ep, ed, Float64[])
    @test sol.ret_val == 1
    @test SCS.raw_status(sol.info) == "solved"
end

# Feasible conic problem with exponential cones (no SDP cones)
# Problem data taken from https://github.com/cvxgrp/scs/blob/master/examples/raw/randomConeFeasible
function feasible_exponential_conic(T)
    n = 24
    m = 71
    z = 10
    l = 10
    bu = Float64[]
    bl = Float64[]
    ep = 10
    ed = 5
    q = [0,1,0,2,3]
    s = Int[]

    b = [
    -0.883995908378185202,  0.247092008779755457,  0.000000000000000000,
    -0.908862166109580594,  0.493908919902298627,  0.215008446366246719,
    -0.756619621167477718, -0.244711519515723036, -2.185656700286640941,
    -1.777454077348217920,  1.191220488872941186, -0.072631913036035556,
    -0.884888299606113149, -3.097264426296667761, -0.491714244502094944,
     0.820612279439398518, -5.027612089838516596, -0.218768971520485273,
    -1.779020210515877221,  0.000000000000000000,  1.049908711514835780,
     0.423746551400514093, -0.078381703192688112, -0.387520318558316479,
     0.959884766721948446, -0.080140759738691447,  1.300716537987696597,
    -0.217623259095584554, -0.772345449741623336, -0.014345948596158398,
     2.002325668310177775,  2.019795133016943467,  1.285967533112210681,
    -0.503689333815666940,  3.004968290408826626, -1.438380292815098382,
    -0.801360722211995502,  0.731209144814713885, -2.639460367287462361,
     1.255602400085220394, -0.301008710647552635,  0.478491654540030953,
    -0.984259121708334739,  0.276318763804415002,  0.631004508865874447,
     1.808914085971600416,  0.379215625232032671, -0.627707287528726510,
    -1.823589846592805763,  0.000000000000000000,  0.376255266738256766,
     0.577717062577191354,  1.119478389802739615,  0.977520182221270906,
     0.000000000000000000,  3.417141674635829851, -0.093825387556597556,
    -1.780472058822893722,  1.589707484189159858,  0.133747881756402398,
     1.316803247114450848, -0.153152367127352751, -1.712195041092097192,
    -0.151457896071195447,  1.216498217552836492,  0.610011299767568738,
     4.095401319683990238, -0.761957853948331154, -0.485158172250699793,
    -1.990510613776122817,  1.151843011380725867]

    c = [
     1.126284961639569149,  0.474697659805094763, -2.123033959497108825,
     4.908405709073863754,  1.205680866991396316, -0.305211910012288978,
    -6.293810724494532494, -0.698794364791622713,  2.279737335846038437,
    -0.945550079347313055, -0.727867522608193762, -1.869349988323733891,
    -3.748045451887451307,  1.125456539838578518,  0.750188573436658457,
     1.255406629974515686, -4.887321053728165055,  4.021564105931883404,
     0.000000000000000000, -5.091850958321861853, -0.068986990722853969,
    -1.715673120575787136,  0.036366854219751360, -3.625427280331933400]

    colptr = [0, 7, 12, 18, 24, 34, 37, 43, 47, 54, 59, 68, 79, 87, 93, 101, 111, 119, 127, 128, 139, 141, 150, 154, 162]

    rowval = [
     5, 17, 22, 31, 55, 65, 69, 12, 18, 30, 34, 58,  9, 11, 13, 18, 30, 66,  0,
    10, 17, 18, 44, 58,  5,  6,  7, 26, 29, 31, 53, 57, 61, 67, 39, 43, 68,  3,
     9, 13, 14, 16, 21,  7, 36, 38, 55,  4, 12, 21, 27, 40, 46, 66,  3, 26, 34,
    44, 53,  5, 11, 17, 27, 31, 37, 42, 45, 63, 13, 22, 24, 33, 36, 40, 50, 56,
    58, 64, 65,  6, 10, 13, 16, 18, 23, 41, 44, 17, 31, 41, 46, 55, 58, 16, 20,
    33, 38, 42, 58, 62, 64,  7, 13, 18, 21, 26, 36, 45, 52, 56, 60,  8, 15, 21,
    30, 33, 48, 53, 64,  1,  5, 11, 16, 23, 24, 28, 39, 37,  5,  8, 28, 30, 41,
    55, 57, 59, 61, 64, 67, 56, 70,  0,  3, 10, 18, 22, 34, 37, 42, 65, 16, 25,
    51, 65,  5, 13, 24, 28, 32, 36, 40, 55]

    values = [
     0.155488995903893978, -0.062791225718324961, -0.723631125289072807,
    -0.238301504589733010, -1.222593380186629552,  0.176577794642316321,
     0.705885019349415832, -1.327043149520291010, -0.054886129988677361,
     0.595357673884101768, -0.608557444738319409, -0.345065971567320995,
     0.312023828329273611,  0.600142508808629960,  0.401844497039814952,
    -1.118732002452724972,  1.046832784305231856,  2.003390610862264776,
    -0.289963040800027938, -0.723121479570418058, -2.021958930051789505,
    -0.626378538867138679,  1.223062551733806957,  1.012801864262980134,
     0.818551368521000522, -0.493009815316450606,  0.045841105713705040,
     0.571247629714476068, -0.131820352915893585,  0.229596893220313791,
     0.245804851893843240, -0.300111005615675885, -0.438141355602143678,
    -0.432003843727780446,  2.787335227813435345,  1.778255899291004472,
    -0.360076301153971601,  1.174116751493714972,  1.804493771724186901,
     1.470201280848519421,  0.397466995795308642,  1.516266896645952711,
     1.808862620519253950, -0.063783120022332648,  0.910482579647111989,
     0.439952188872439920,  0.316500360797716096, -1.481399071578779925,
    -1.441013597638914456, -1.079866250738828137,  0.759568325914783404,
    -1.854299082689693901,  0.066756911436865754,  0.950993499549085408,
     0.126947068043645916,  0.412796010311391848, -0.737059771697805455,
    -1.283256104604766579,  0.070045209416807905, -0.292588130834394000,
     0.593930795648752730, -0.982131525779047831, -0.657201299098350344,
     0.439997904822629349,  0.183703423091249041,  0.541334435719376117,
     0.901931466951714444, -0.297697144009373027, -0.326814228785906158,
    -0.593250315010803608, -0.373070658631004248,  0.601102032468295078,
     0.867082552947325391, -1.140681144669632330, -0.069214254022048366,
    -0.418903195029848852,  0.629334584931418806,  1.143678910770957513,
     0.970737823554688517, -0.180739356415037467,  0.526547037749559022,
     0.812323004640795987, -0.032566509194480744,  0.249517740562795132,
     0.942133319236942435, -0.433609296474823602, -2.328954516283341825,
     0.612511298166949270, -0.616865928889227422, -0.168469878275448348,
     0.035479485837577006, -1.342869236621930140, -0.213015082641054704,
     1.635999657278292441, -0.640709506726057532,  0.092307951238962266,
     0.101662443700341151,  0.389266203843263880, -0.865697308360523721,
     0.983545235205556256, -0.531620117507069279,  0.611335194065235954,
     0.545540103526115816, -0.993019006549600625,  0.199189444075264455,
    -0.986961883351521907, -0.079892839058037096, -1.835638683735192345,
     0.235809672576244050, -0.140321722056366061, -0.270068812648098844,
     0.109317693774870342, -0.751894738681891606, -1.521026561768954677,
    -0.197958632611841989,  1.729841391572364273,  2.227168078167871546,
    -0.608580510079949288,  0.972565728008653152,  0.475424811707271355,
    -0.540786416488525767, -2.186021612748235476, -0.425058490610030815,
     0.300485967595730263,  0.815488508092498399, -0.603918481376169214,
    -1.166665030194641073,  0.290790134884453599, -0.308641815280113085,
     1.814015450280375585,  0.176946822329411252,  0.327678163907200726,
    -0.218533560026580531, -1.032184344072171189,  1.029365712103099240,
    -1.043108301337626775, -0.408674314796325655, -0.522250484993548869,
     0.648940737363586706,  0.899822328897223467,  1.415849068436585423,
     1.261550718141148275, -0.656815928948082495, -0.260250861706116776,
     0.974950224811311550,  0.401336339818801069, -1.749879306387625455,
     0.112944717021051230,  0.751228984688332879, -0.413972274974230170,
     0.589433366718220664,  0.120205281956355367, -0.507323064614498387,
    -0.438270518204901538, -1.096593301525472075, -1.051632309520848407,
     0.798886992156715880, -0.307503469862750567,  0.274836786911666187,
     0.898475989377141793, -1.093343456239604494,  1.331215885064973348]

    A = SparseMatrixCSC(m, n, colptr .+ 1, rowval .+ 1, vec(values))
    P = spzeros(n, n)

    sol = scs_solve(T, m, n, A, P, b, c, z, l, bu, bl, q, s, ep, ed, Float64[])
    @assert sol.ret_val == 1
    @test SCS.raw_status(sol.info) == "solved"
end

# Feasible conic problem with exponential and SDP cones
# BLAS and LAPACK must be linked
# Problem data taken from https://github.com/cvxgrp/scs/blob/master/examples/raw/randomConeFeasibleSDP
function feasible_sdp_conic(T)
    n = 15
    m = 46
    z = 3
    bu= Float64[]
    bl= Float64[]
    l = 3
    ep = 2
    ed = 2
    q = [2,3,4]
    s = [2,3,4]

    b = [
    -0.757244022903780567,  0.000000000000000000, -1.892151461056349238,
    -0.163643541176375057,  0.000000000000000000, -1.081834184990333014,
     0.795686782662209935, -0.459554179861930256,  0.000000000000000000,
    -0.021401100284139062,  0.221822131770426145,  0.624165617064677769,
     0.103992209313486683,  0.383906342184775817,  0.220548856781228653,
     0.207179552343107032, -0.251552024267611252, -0.940985378402204531,
     0.143203475268773417, -0.637990380166112425, -0.047725168844638927,
    -1.400142610548834821, -0.518640886812063995, -0.466344476203716551,
    -0.306977592331722393, -0.733266642509912470,  1.636614964482892454,
     2.801678431332372821,  0.542022232315772401,  0.561733632830300200,
    -2.792150430434448882,  0.679252001011406148,  0.060935700608246268,
     1.017184097064012693, -1.472726175247485836,  0.298927350489858801,
     0.283700004781893000, -5.946289176766917706,  0.563505302268017871,
     1.728907659188164914,  0.783139219605003434,  0.241447041607357943,
    -0.100769851306178021, -0.312858596637428432,  0.715583273231235939,
     0.030051296196268559]

    c = [
    -0.159052113684804763,  1.195574970454808206,  0.354942838344536771,
    -0.144003787891437030,  0.031108987439201756, -6.024128094309364911,
    -6.684198760055235056, -2.875770597510225901, -1.958442177032014397,
    -3.231061172173861795, -0.047201649069278366, -1.907845683517030544,
     0.605113199091042553,  2.360164164701050726, -0.367619298880400125]
    colptr = [0, 3, 11, 18, 24, 25, 30, 36, 39, 44, 47, 48, 51, 56, 61, 66]
    rowval = [
     7, 26, 27,  3,  5,  9, 15, 26, 28, 31, 34,  7, 12, 21, 25, 32, 38, 44, 10,
    11, 16, 35, 36, 40, 10, 11, 14, 24, 26, 34, 11, 12, 16, 20, 38, 42, 13, 34,
    42,  3,  6, 11, 15, 29, 30, 34, 37, 31,  2,  5, 42, 11, 20, 22, 24, 34,  6,
    17, 23, 25, 44,  0, 21, 30, 32, 42];
    values = [
    -0.067865553542687335,  1.530072514424096086,  1.603457298120044117,
    -0.334886938964047698,  1.039090653504955997, -0.217606350143191934,
     0.307061919146703444, -0.249024742513713787,  1.234679146890777846,
    -0.155941035724768945,  0.391894209432449070, -0.195221197898754362,
     0.625190357087625714, -0.532011376808820713, -0.192239517539274762,
    -0.261163645776478603, -0.457014640871582556, -1.565056014150725039,
    -0.303107621351740908,  0.051290355848774699,  0.515246335524848553,
    -0.320575506600239257,  0.012469041361617951, -1.066701398984749583,
     0.023045624425105282,  0.826062790211595455,  0.949221831131022542,
    -0.712004549027422495, -1.064213412889326804, -1.250678906826407477,
     1.526976686733372723,  0.183227263001436963,  0.261406324055382666,
    -0.162337672803827743,  1.242448406390738391,  0.933728162671238482,
    -1.029767543566621146, -0.947960922331432032,  0.350321001356111605,
     0.552783345944550142,  1.260658709120896281,  0.466914435684700035,
     0.135174942099455658, -0.229626450963180490, -1.506159703979718989,
    -0.741106093940411492, -3.029177341404145629,  0.276068253931536167,
    -0.848709379933659025, -1.117638683265208099, -0.029005763708726321,
    -0.209713338388736709, -0.146054634331526184, -0.875729346160017297,
    -1.174212331456816250, -0.507817550278173657,  0.660143141046977688,
    -0.941485770955433732, -0.483815050110121103, -0.274070229932602216,
    -0.084539479817724195,  0.455029556444334349,  1.682103594663178825,
    -0.444627816446985402,  0.443421912904091331,  0.182452167505983420]
    A = SparseMatrixCSC(m, n, colptr .+ 1, rowval .+ 1, vec(values))
    P = spzeros(n, n)
    sol = scs_solve(T, m, n, A, P, b, c, z, l, bu, bl, q, s, ep, ed, Float64[])
    @test sol.ret_val == 1
    @test SCS.raw_status(sol.info) == "solved"
end

# Feasible conic problem with power cones
function feasible_pow_conic(T)
    n = 9
    m = 28
    z = 5
    l = 5
    bu = Float64[]
    bl = Float64[]
    ep = 0
    ed = 0
    q = Int[]
    s = Int[]
    p = [-3.0e-01, 2.5e-01, 7.5e-01, -4.0e-01, 8.7e-01, -1.2e-01]
    b = [
    -0.887386390749058451,  1.868842541924223610, -0.755288651776904296,
     0.000000000000000000,  0.000000000000000000,  0.000000000000000000,
    -0.242379421582613414,  0.000000000000000000,  0.000000000000000000,
     0.000000000000000000,  2.000257252427916743,  0.841870176542910631,
    -1.245982120437818974,  0.644136460056986104, -0.068037985179701416,
     0.041769615282582828,  1.081088773255733271,  1.417941447788496845,
     0.955133254978043800,  2.477002457942629299,  0.326964373865085811,
     0.322926943542406031,  0.765172439731569787,  1.673745381540497679,
     0.056224953164704453,  0.024308325296160227, -0.492458408194795028,
    -0.193643481641372683]
    c = [
     0.581324380640174887,  0.000000000000000000, -1.274914899429569370,
     1.437959547820705897,  0.178499636115030086, -2.834480531039399676,
    -0.465471220509626749,  0.440187271984058437,  1.655006387767208542];
    colptr = [0, 2, 2, 8, 11, 12, 15, 21, 22, 25]
    rowval = [11, 19, 10, 11, 14, 18, 19, 26, 1, 19, 23, 2, 1, 14, 19, 0, 1, 10, 14, 23, 26, 16, 0, 6, 18]
    values = [
    -0.511172207780700494, -0.557093642241281661, -0.322939921204497105,
    -0.002041345349432955,  1.606510961119237413, -0.070499387778693703,
    -0.336705699002853465,  1.122647857944874650,  0.375041023696103593,
     0.415227462723155749, -2.444298897865559539, -2.377454293765433491,
     1.125161817875028225,  0.847648634500925313,  1.557813537123208025,
     0.400486023191097329,  0.728641591773904751,  0.317987915650739417,
     0.268100811901575309, -1.098195387799324019,  0.581667258045274083,
    -0.923489085784077401, -1.341380722378574086, -0.273782415743900387,
     0.147891351014747152]
    A = SparseMatrixCSC(m, n, colptr .+ 1, rowval .+ 1, vec(values))
    P = spzeros(n, n)
    sol = scs_solve(T, m, n, A, P, b, c, z, l, bu, bl, q, s, ep, ed, p)
    @test sol.ret_val == 1
    @test SCS.raw_status(sol.info) == "solved"
    return
end

function feasible_basic_problems(solver)
    A = reshape([1.0], (1, 1))
    P = spzeros(1, 1)
    solution = scs_solve(
        solver,
        1,
        1,
        A,
        P,
        [1.0],
        [1.0],
        1,
        0,
        Float64[],
        Float64[],
        Int[],
        Int[],
        0,
        0,
        Float64[],
    );
    @test solution.ret_val == 1
    feasible_basic_conic(solver)
    feasible_exponential_conic(solver)
    feasible_sdp_conic(solver)
    feasible_pow_conic(solver)
    return
end

function test_options(T)
    A = [
        1.0 1.0 0.0 0.0 0.0
        0.0 1.0 0.0 0.0 1.0
        0.0 0.0 1.0 1.0 1.0
        -1.0 0.0 0.0 0.0 0.0
        0.0 -1.0 0.0 0.0 0.0
        0.0 0.0 -1.0 0.0 0.0
        0.0 0.0 0.0 -1.0 0.0
        0.0 0.0 0.0 0.0 -1.0
    ]
    m, n = size(A)
    args = (
        m = m,
        n = n,
        A = A,
        P = zeros(n, n),
        b = [5.0, 3.0, 9.0, 0.0, 0.0, 0.0, 0.0, 0.0],
        c = -[3.0, 4.0, 4.0, 9.0, 5.0],
        z = 0,
        l = 8,
        bu = Float64[],
        bl = Float64[],
        q = Int64[],
        s = Int64[],
        ep = 0,
        ed = 0,
        p = Float64[],
    )
    solution = SCS.scs_solve(T, args...)
    @test isapprox(solution.x' * args.c, -99.0; rtol = 1e-4)
    @test !isapprox(solution.x' * args.c, -99.0; rtol = 1e-7)
    solution = SCS.scs_solve(T, args...; eps_abs = 1e-10, eps_rel = 1e-10)
    @test isapprox(solution.x' * args.c, -99.0; rtol = 1e-10)
    solution = SCS.scs_solve(
        T,
        args...,
        solution.x,
        solution.y,
        solution.s;
        max_iters = 1,
        warm_start = true,
        eps_abs = 1e-10,
        eps_rel = 1e-10,
    )
    @test isapprox(solution.x' * args.c, -99.0; rtol = 1e-9)
    @test_throws(ArgumentError, SCS.scs_solve(T, args...; eps = 1e-12))
    err = try
        SCS.scs_solve(T, args...; eps_abs = 1e-12, eps = 1e-12)
    catch ex
        ex
    end
    @test err.msg == "Unrecognized option passed to SCS solver: eps"

    tmpf = tempname()
    @test !isfile(tmpf)
    SCS.scs_solve(T, args...; eps_abs = 1e-12, write_data_filename = tmpf)
    @test isfile(tmpf)

    @test_throws ErrorException SCS.scs_solve(T, args...; write_data_filename = @view tmpf[1:end])
    return
end