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Maros-Meszaros sparse subset

Number of problems 76
Benchmark version 2.3.0
Date 2024-09-07 15:23:00.654152+00:00
CPU Intel(R) Core(TM) i7-6500U CPU @ 2.50GHz
Run by @stephane-caron

Benchmark reports are copious as we aim to document comparison factors as much as possible. You can also jump to results directly.

Contents

Description

Subset of the Maros-Meszaros test set complementary to the dense subset.

Solvers

solver version
clarabel 0.9.0
cvxopt 1.3.2
gurobi 11.0.3 (size-limited)
highs 1.7.2
osqp 0.6.7.post0
piqp 0.4.1
proxqp 0.6.6
qpalm 1.2.3
scs 3.2.6

All solvers were called via qpsolvers v4.3.3.

CPU info

Property Value
arch X86_64
arch_string_raw x86_64
bits 64
brand_raw Intel(R) Core(TM) i7-6500U CPU @ 2.50GHz
count 4
cpuinfo_version_string 9.0.0
family 6
flags 3dnowprefetch, abm, acpi, adx, aes, aperfmperf, apic, arat, arch_capabilities, arch_perfmon, art, avx, avx2, bmi1, bmi2, bts, clflush, clflushopt, cmov, constant_tsc, cpuid, cpuid_fault, cx16, cx8, de, ds_cpl, dtes64, dtherm, dts, epb, ept, ept_ad, erms, est, f16c, flexpriority, flush_l1d, fma, fpu, fsgsbase, fxsr, ht, hwp, hwp_act_window, hwp_epp, hwp_notify, ibpb, ibrs, ida, intel_pt, invpcid, invpcid_single, lahf_lm, lm, mca, mce, md_clear, mmx, monitor, movbe, mpx, msr, mtrr, nonstop_tsc, nopl, nx, osxsave, pae, pat, pbe, pcid, pclmulqdq, pdcm, pdpe1gb, pebs, pge, pln, pni, popcnt, pse, pse36, pti, pts, rdrand, rdrnd, rdseed, rdtscp, rep_good, sdbg, sep, sgx, smap, smep, ss, ssbd, sse, sse2, sse4_1, sse4_2, ssse3, stibp, syscall, tm, tm2, tpr_shadow, tsc, tsc_adjust, tsc_deadline_timer, tscdeadline, vme, vmx, vnmi, vpid, x2apic, xgetbv1, xsave, xsavec, xsaveopt, xsaves, xtopology, xtpr
hz_actual_friendly 2.6000 GHz
hz_advertised_friendly 2.5000 GHz
l1_data_cache_size 65536
l1_instruction_cache_size 65536
l2_cache_associativity 6
l2_cache_line_size 256
l2_cache_size 524288
l3_cache_size 4194304
model 78
python_version 3.12.4.final.0 (64 bit)
stepping 3
vendor_id_raw GenuineIntel

Settings

There are 4 settings: default, high_accuracy, low_accuracy and mid_accuracy. They validate solutions using the following tolerances:

tolerance default high_accuracy low_accuracy mid_accuracy
dual 1 1e-09 0.001 1e-06
gap 1 1e-09 0.001 1e-06
primal 1 1e-09 0.001 1e-06
runtime 1000 1000 1000 1000

Solvers for each settings are configured as follows:

solver parameter default high_accuracy low_accuracy mid_accuracy
clarabel tol_feas - 1e-09 0.001 1e-06
clarabel tol_gap_abs - 1e-09 0.001 1e-06
clarabel tol_gap_rel - 0 0 0
cvxopt feastol - 1e-09 0.001 1e-06
gurobi FeasibilityTol - 1e-09 0.001 1e-06
gurobi OptimalityTol - 1e-09 0.001 1e-06
gurobi TimeLimit 1000.0 1000 1000 1000
highs dual_feasibility_tolerance - 1e-09 0.001 1e-06
highs primal_feasibility_tolerance - 1e-09 0.001 1e-06
highs time_limit 1000.0 1000 1000 1000
osqp eps_abs - 1e-09 0.001 1e-06
osqp eps_rel - 0 0 0
osqp time_limit 1000.0 1000 1000 1000
piqp check_duality_gap - 1 1 1
piqp eps_abs - 1e-09 0.001 1e-06
piqp eps_duality_gap_abs - 1e-09 0.001 1e-06
piqp eps_duality_gap_rel - 0 0 0
piqp eps_rel - 0 0 0
proxqp check_duality_gap - 1 1 1
proxqp eps_abs - 1e-09 0.001 1e-06
proxqp eps_duality_gap_abs - 1e-09 0.001 1e-06
proxqp eps_duality_gap_rel - 0 0 0
proxqp eps_rel - 0 0 0
qpalm eps_abs - 1e-09 0.001 1e-06
qpalm eps_rel - 0 0 0
qpalm time_limit 1000.0 1000 1000 1000
scs eps_abs - 1e-09 0.001 1e-06
scs eps_rel - 0 0 0
scs time_limit_secs 1000.0 1000 1000 1000

Known limitations

The following issues have been identified as impacting the fairness of this benchmark. Keep them in mind when drawing conclusions from the results.

  • #60: Conversion to SOCP limits performance of ECOS
  • #88: CPU thermal throttling

Results by settings

Default

Solvers are compared over the whole test set by shifted geometric mean (shm). Lower is better, 1.0 is the best.

Success rate (%) Runtime (shm) Primal residual (shm) Dual residual (shm) Duality gap (shm)
clarabel 40.8 118.3 702.2 11838.5 1.0
cvxopt 42.1 110.1 575.2 9648.1 4.2
gurobi 1.3 680.4 1192.9 20034.2 1.7
highs 61.8 36.5 354.0 6003.9 9.5
osqp 36.8 9.5 3941.8 56127.5 337.4
piqp 93.4 1.0 1.0 1.0 1.9
proxqp 63.2 73.4 369.6 6207.1 2.5
qpalm 52.6 3.2 784.0 14213.7 77.2
scs 55.3 17.6 2916.3 12988.0 13.9

High accuracy

Solvers are compared over the whole test set by shifted geometric mean (shm). Lower is better, 1.0 is the best.

Success rate (%) Runtime (shm) Primal residual (shm) Dual residual (shm) Duality gap (shm)
clarabel 27.6 10.3 3.3 6536.6 36.0
cvxopt 3.9 9.8 2549480.8 2.0 1753480544.8
gurobi 1.3 52.0 4.7 2.5 1.0
highs 0.0 3.5 11564.5 7165193.1 5554599469.9
osqp 13.2 17.4 3.8 2.0 4100.2
piqp 67.1 1.0 1.0 1.0 6827.6
proxqp 34.2 10.1 2.9 2.1 2.1
qpalm 7.9 4.3 3.7 1.3 1717432.3
scs 23.7 15.7 3.8 1.9 1.0

Low accuracy

Solvers are compared over the whole test set by shifted geometric mean (shm). Lower is better, 1.0 is the best.

Success rate (%) Runtime (shm) Primal residual (shm) Dual residual (shm) Duality gap (shm)
clarabel 44.7 60.9 1.7 9.9 1.9
cvxopt 34.2 64.7 3.2 6.3 5919.2
gurobi 1.3 484.1 3.2 7.3 2.7
highs 27.6 25.3 1.0 24.1 14422.5
osqp 6.6 30.0 2.3 4.4 5805.3
piqp 94.7 1.0 3.4 1.0 1.3
proxqp 61.8 47.2 1.3 2.7 1.8
qpalm 10.5 3.1 1.5 1.0 20732.3
scs 67.1 19.0 36.7 3.3 1.0

Mid accuracy

Solvers are compared over the whole test set by shifted geometric mean (shm). Lower is better, 1.0 is the best.

Success rate (%) Runtime (shm) Primal residual (shm) Dual residual (shm) Duality gap (shm)
clarabel 38.2 35.4 11.0 36.6 1.2
cvxopt 13.2 30.3 10230.8 4.3 3387961.2
gurobi 1.3 213.0 18.7 8.3 1.6
highs 1.3 12.1 52.3 23640.0 8676612.9
osqp 10.5 56.0 14.8 6.5 42153.8
piqp 90.8 1.0 1.0 1.0 166.4
proxqp 50.0 34.4 9.7 4.2 1.0
qpalm 13.2 1.8 7.8 2.0 3091085.5
scs 46.1 26.3 17.9 4.7 1.1

Results by metric

Success rate

Precentage of problems each solver is able to solve:

default high_accuracy low_accuracy mid_accuracy
clarabel 41 28 45 38
cvxopt 42 4 34 13
gurobi 1 1 1 1
highs 62 0 28 1
osqp 37 13 7 11
piqp 93 67 95 91
proxqp 63 34 62 50
qpalm 53 8 11 13
scs 55 24 67 46

Rows are solvers and columns are settings. We consider that a solver successfully solved a problem when (1) it returned with a success status and (2) its solution satisfies optimality conditions within tolerance. The second table below summarizes the frequency at which solvers return success (1) and the corresponding solution did indeed pass tolerance checks.

Percentage of problems where "solved" return codes are correct:

default high_accuracy low_accuracy mid_accuracy
clarabel 100 89 97 96
cvxopt 91 58 79 59
gurobi 100 100 100 100
highs 92 34 58 33
osqp 54 88 47 80
piqp 93 88 96 96
proxqp 95 91 97 100
qpalm 54 51 14 18
scs 75 92 93 96

Computation time

We compare solver computation times over the whole test set using the shifted geometric mean. Intuitively, a solver with a shifted-geometric-mean runtime of Y is Y times slower than the best solver over the test set. See Metrics for details.

Shifted geometric mean of solver computation times (1.0 is the best):

default high_accuracy low_accuracy mid_accuracy
clarabel 118.3 10.3 60.9 35.4
cvxopt 110.1 9.8 64.7 30.3
gurobi 680.4 52.0 484.1 213.0
highs 36.5 3.5 25.3 12.1
osqp 9.5 17.4 30.0 56.0
piqp 1.0 1.0 1.0 1.0
proxqp 73.4 10.1 47.2 34.4
qpalm 3.2 4.3 3.1 1.8
scs 17.6 15.7 19.0 26.3

Rows are solvers and columns are solver settings. The shift is sh = 10. As in the OSQP and ProxQP benchmarks, we assume a solver's run time is at the time limit when it fails to solve a problem.

Optimality conditions

Primal residual

The primal residual measures the maximum (equality and inequality) constraint violation in the solution returned by a solver. We use the shifted geometric mean to compare solver primal residuals over the whole test set. Intuitively, a solver with a shifted-geometric-mean primal residual of Y is Y times less precise on constraints than the best solver over the test set. See Metrics for details.

Shifted geometric means of primal residuals (1.0 is the best):

default high_accuracy low_accuracy mid_accuracy
clarabel 702.2 3.3 1.7 11.0
cvxopt 575.2 2549480.8 3.2 10230.8
gurobi 1192.9 4.7 3.2 18.7
highs 354.0 11564.5 1.0 52.3
osqp 3941.8 3.8 2.3 14.8
piqp 1.0 1.0 3.4 1.0
proxqp 369.6 2.9 1.3 9.7
qpalm 784.0 3.7 1.5 7.8
scs 2916.3 3.8 36.7 17.9

Rows are solvers and columns are solver settings. The shift is sh = 10. A solver that fails to find a solution receives a primal residual equal to the full primal tolerance.

Dual residual

The dual residual measures the maximum violation of the dual feasibility condition in the solution returned by a solver. We use the shifted geometric mean to compare solver dual residuals over the whole test set. Intuitively, a solver with a shifted-geometric-mean dual residual of Y is Y times less precise on the dual feasibility condition than the best solver over the test set. See Metrics for details.

Shifted geometric means of dual residuals (1.0 is the best):

default high_accuracy low_accuracy mid_accuracy
clarabel 11838.5 6536.6 9.9 36.6
cvxopt 9648.1 2.0 6.3 4.3
gurobi 20034.2 2.5 7.3 8.3
highs 6003.9 7165193.1 24.1 23640.0
osqp 56127.5 2.0 4.4 6.5
piqp 1.0 1.0 1.0 1.0
proxqp 6207.1 2.1 2.7 4.2
qpalm 14213.7 1.3 1.0 2.0
scs 12988.0 1.9 3.3 4.7

Rows are solvers and columns are solver settings. The shift is sh = 10. A solver that fails to find a solution receives a dual residual equal to the full dual tolerance.

Duality gap

The duality gap measures the consistency of the primal and dual solutions returned by a solver. A duality gap close to zero ensures that the complementarity slackness optimality condition is satisfied. We use the shifted geometric mean to compare solver duality gaps over the whole test set. Intuitively, a solver with a shifted-geometric-mean duality gap of Y is Y times less precise on the complementarity slackness condition than the best solver over the test set. See Metrics for details.

Shifted geometric means of duality gaps (1.0 is the best):

default high_accuracy low_accuracy mid_accuracy
clarabel 1.0 36.0 1.9 1.2
cvxopt 4.2 1753480544.8 5919.2 3387961.2
gurobi 1.7 1.0 2.7 1.6
highs 9.5 5554599469.9 14422.5 8676612.9
osqp 337.4 4100.2 5805.3 42153.8
piqp 1.9 6827.6 1.3 166.4
proxqp 2.5 2.1 1.8 1.0
qpalm 77.2 1717432.3 20732.3 3091085.5
scs 13.9 1.0 1.0 1.1

Rows are solvers and columns are solver settings. The shift is sh = 10. A solver that fails to find a solution receives a duality gap equal to the full gap tolerance.