Unexpected 'Infeasible' Results with SCS 3.2.4 on QP Problems for MPC #275

kkzhang3 · 2024-06-25T08:08:54Z

Hi,
I am using SCS 3.2.4 with Python 3.12 on a Windows 11 machine to solve Quadratic Programming (QP) problems for Model Predictive Control (MPC). Most of the optimization results are correct, and the speed is very impressive. However, I encountered three instances where the solver returned 'infeasible' results unexpectedly.

Details:
SCS Version: 3.2.4
Python Version: 3.12
Operating System: Windows 11

To verify the results, I also solved the same problems using CLARABEL and CVXOPT, both of which returned 'optimal' results. I have attached the three problems that resulted in 'infeasible' status with SCS for your reference.

QPerr.zip

I turned on the export file options and get the files:
data_and_logs.zip

Thanks
Regards,
KK Zhang

bodono · 2024-06-25T15:26:36Z

Thanks for posting this. For some reason when I run your python code I actually get 'solved' correctly form SCS (I tuned on verbose outputs from SCS to see what was happening):

=============SCS_DynamicLayer_20240625_124826_169446.pickle=============
------------------------------------------------------------------
	       SCS v3.2.4 - Splitting Conic Solver
	(c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------
problem:  variables n: 45, constraints m: 120
cones: 	  l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07
	  alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
	  max_iters: 100000, normalize: 1, rho_x: 1.00e-06
	  acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
	  nnz(A): 770, nnz(P): 571
------------------------------------------------------------------
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.27e+02  9.56e+04  2.61e+06 -1.92e+04  1.00e-01  5.62e-04
   125| 8.56e-07  7.87e-07  6.64e-10 -4.74e-01  2.60e-06  1.12e-03
------------------------------------------------------------------
status:  solved
timings: total: 1.36e-03s = setup: 4.88e-04s + solve: 8.76e-04s
	 lin-sys: 3.98e-04s, cones: 1.98e-05s, accel: 9.42e-06s
------------------------------------------------------------------
objective = -0.473958
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = -0.4739577941554467, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([2.79051355e-03, 2.51015653e-03, 2.28324465e-03, 2.06283283e-03,
       1.86450968e-03, 1.68458892e-03, 1.51852915e-03, 1.39307046e-03,
       1.25959954e-03, 6.36925603e-04, 2.20305700e-03, 2.55722225e-03,
       2.01489587e-03, 1.11603498e-03, 8.82347315e-13, 2.44155017e-13,
       2.44155052e-13, 2.44154819e-13, 2.44154573e-13, 2.44154880e-13,
       2.44154818e-13, 2.44154854e-13, 2.44154832e-13, 2.44154613e-13,
       2.43690456e-13, 2.42395011e-13, 2.39947370e-13, 2.32628320e-13,
       2.28770011e-13, 2.25283848e-13, 2.19736734e-13, 2.17522596e-13,
       2.15555946e-13, 2.13101029e-13, 2.12847980e-13, 2.12966041e-13,
       2.13656973e-13, 2.13847931e-13, 2.13606401e-13, 2.12391170e-13,
       2.11936694e-13, 2.11449525e-13, 2.09675861e-13, 2.08817582e-13,
       2.08428513e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.47395779415549794, clarabel_solution.status = Solved, clarabel_solution.x = [0.0027904978965449666, 0.002510198944930076, 0.0022831938027705117, 0.0020628662164093115, 0.0018645011729976001, 0.0016845818065795019, 0.001518539175488385, 0.0013930633137464905, 0.0012596049961827414, 0.0006369214890962764, 0.0022030588661007553, 0.0025572221591200366, 0.0020148959955852168, 0.0011160350187950417, 7.508467158983145e-12, 5.098200436696795e-11, 5.0919837101523314e-11, 5.086396393312051e-11, 5.076849130648014e-11, 5.0727771773585275e-11, 5.0691087613373826e-11, 5.06282096624894e-11, 5.060131621510247e-11, 5.057704866048211e-11, 5.069986578401336e-11, 5.116678636363365e-11, 5.302258154382985e-11, 5.873031052501555e-11, 6.015784957736915e-11, 6.028859646361538e-11, 5.858461187983256e-11, 5.749518217098852e-11, 5.6554606511962526e-11, 5.5554309438862856e-11, 5.550824040336608e-11, 5.5663838781453607e-11, 5.622236098014281e-11, 5.650877287399536e-11, 5.67251738200511e-11, 5.689231675566984e-11, 5.6871853878784136e-11, 5.681457007507406e-11, 5.6676452814081476e-11, 5.6626453715519324e-11, 5.659015006674711e-11]
=============SCS_DynamicLayer_20240625_124826_273466.pickle=============
------------------------------------------------------------------
	       SCS v3.2.4 - Splitting Conic Solver
	(c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------
problem:  variables n: 45, constraints m: 120
cones: 	  l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07
	  alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
	  max_iters: 100000, normalize: 1, rho_x: 1.00e-06
	  acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
	  nnz(A): 770, nnz(P): 571
------------------------------------------------------------------
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.27e+02  9.55e+04  2.61e+06 -1.87e+04  1.00e-01  4.21e-04
   125| 1.13e-06  5.85e-07  1.65e-10 -2.93e-02  1.67e-06  9.57e-04
------------------------------------------------------------------
status:  solved
timings: total: 9.61e-04s = setup: 3.57e-04s + solve: 6.05e-04s
	 lin-sys: 3.97e-04s, cones: 2.77e-05s, accel: 9.54e-06s
------------------------------------------------------------------
objective = -0.029337
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = -0.02933683070721473, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([6.94261027e-04, 6.24498710e-04, 5.68067204e-04, 5.13205792e-04,
       4.63877735e-04, 4.19117973e-04, 3.77790668e-04, 3.46590440e-04,
       3.13374278e-04, 1.58465232e-04, 5.48101854e-04, 6.36216877e-04,
       5.01290169e-04, 2.77660710e-04, 8.97944892e-13, 2.44076024e-13,
       2.44076057e-13, 2.44075891e-13, 2.44076235e-13, 2.44076090e-13,
       2.44076115e-13, 2.44076194e-13, 2.44076076e-13, 2.44076086e-13,
       2.43611958e-13, 2.42316618e-13, 2.39869035e-13, 2.32552681e-13,
       2.28695071e-13, 2.25209971e-13, 2.19664083e-13, 2.17450745e-13,
       2.15484662e-13, 2.13030405e-13, 2.12777627e-13, 2.12895381e-13,
       2.13586682e-13, 2.13777081e-13, 2.13535797e-13, 2.12320797e-13,
       2.11866598e-13, 2.11379410e-13, 2.09606049e-13, 2.08748222e-13,
       2.08359357e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.029336830707231296, clarabel_solution.status = Solved, clarabel_solution.x = [0.0006942523340928437, 0.0006245211531611647, 0.0005680402154852255, 0.0005132247970703782, 0.0004638732525990768, 0.00041911069714191607, 0.0003778005965243507, 0.0003465831910797031, 0.00031337981165299804, 0.00015846105492833785, 0.0005481037104770503, 0.0006362167512226497, 0.0005012902690682797, 0.00027766073090962665, 7.422584398118871e-12, 4.9908818125428526e-11, 4.989439411164203e-11, 4.988135885249981e-11, 4.985892511625296e-11, 4.9849294577522e-11, 4.9840585958786774e-11, 4.982558652410234e-11, 4.9819142820566604e-11, 4.981331356680122e-11, 4.996139585601154e-11, 5.043061010730071e-11, 5.2267431264695483e-11, 5.791302606624232e-11, 5.933302998604015e-11, 5.947440433414254e-11, 5.7813882893553324e-11, 5.674740148239301e-11, 5.582688709371417e-11, 5.485106791023405e-11, 5.481192913018784e-11, 5.497105449305245e-11, 5.55308513300546e-11, 5.5818938404726124e-11, 5.603741130163767e-11, 5.620771956517265e-11, 5.618918130039591e-11, 5.61345389366892e-11, 5.6001086068957654e-11, 5.595235055766623e-11, 5.5916314191366856e-11]
=============SCS_DynamicLayer_20240625_124826_366924.pickle=============
------------------------------------------------------------------
	       SCS v3.2.4 - Splitting Conic Solver
	(c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------
problem:  variables n: 45, constraints m: 120
cones: 	  l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07
	  alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
	  max_iters: 100000, normalize: 1, rho_x: 1.00e-06
	  acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
	  nnz(A): 770, nnz(P): 571
------------------------------------------------------------------
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.26e+02  9.55e+04  2.61e+06 -1.86e+04  1.00e-01  4.15e-04
   125| 1.18e-06  4.87e-07  1.03e-10 -2.22e-03  1.08e-06  9.46e-04
------------------------------------------------------------------
status:  solved
timings: total: 9.54e-04s = setup: 3.51e-04s + solve: 6.03e-04s
	 lin-sys: 4.00e-04s, cones: 1.99e-05s, accel: 9.37e-06s
------------------------------------------------------------------
objective = -0.002215
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = -0.002215217426750132, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([1.90769690e-04, 1.71594496e-04, 1.56110165e-04, 1.41011648e-04,
       1.27465265e-04, 1.15173374e-04, 1.03803298e-04, 9.52430020e-05,
       8.61068673e-05, 4.35357962e-05, 1.50626706e-04, 1.74800288e-04,
       1.37827614e-04, 7.63372245e-05, 9.03386108e-13, 2.44050817e-13,
       2.44050751e-13, 2.44050780e-13, 2.44050750e-13, 2.44050759e-13,
       2.44050816e-13, 2.44050761e-13, 2.44050817e-13, 2.44050791e-13,
       2.43586577e-13, 2.42291618e-13, 2.39844182e-13, 2.32528051e-13,
       2.28670874e-13, 2.25186067e-13, 2.19640423e-13, 2.17427416e-13,
       2.15461294e-13, 2.13007261e-13, 2.12754315e-13, 2.12872044e-13,
       2.13563177e-13, 2.13753984e-13, 2.13512252e-13, 2.12297703e-13,
       2.11843459e-13, 2.11356310e-13, 2.09583286e-13, 2.08725420e-13,
       2.08336267e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.0022152174267624503, clarabel_solution.status = Solved, clarabel_solution.x = [0.00019076221443803517, 0.00017161401041454087, 0.00015608744276640255, 0.00014102549607533437, 0.00012746475443084693, 0.00011516469875157536, 0.00010381329027656681, 9.523573477537405e-05, 8.611244900019338e-05, 4.353157804871756e-05, 0.0001506285695843726, 0.00017480015346372276, 0.0001378277079923815, 7.633724223309769e-05, 7.402155371667622e-12, 4.965827303819483e-11, 4.965437612207606e-11, 4.9650849634359917e-11, 4.9644769875527325e-11, 4.9642155753222594e-11, 4.963978972099507e-11, 4.963570972671434e-11, 4.9633955095898954e-11, 4.963236680374209e-11, 4.9786226003858936e-11, 5.025590754635561e-11, 5.2088158356424624e-11, 5.771888610298373e-11, 5.913705369744592e-11, 5.928091578082756e-11, 5.763067279392684e-11, 5.6569626886655146e-11, 5.565386489883837e-11, 5.4683841907138966e-11, 5.464633867216354e-11, 5.48062925637533e-11, 5.5366378165935806e-11, 5.5654855288171645e-11, 5.587381381126671e-11, 5.6044868611528313e-11, 5.602678592850865e-11, 5.5972769367126994e-11, 5.584042230149203e-11, 5.5791986607535523e-11, 5.575601417531867e-11]

bodono · 2024-06-25T15:27:14Z

Can you post the output of what you get when you run with verbose=True? It might be that something is compiled wrongly in your setup somehow.

kkzhang3 · 2024-06-25T23:22:15Z

Sure. These are the results.

scs.__version__ = '3.2.4'
clarabel.__version__ = '0.7.1'
=============SCS_DynamicLayer_20240625_124826_169446.pickle=============
------------------------------------------------------------------      
               SCS v3.2.4 - Splitting Conic Solver
        (c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------      
problem:  variables n: 45, constraints m: 120
cones:    l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07       
          alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
          max_iters: 100000, normalize: 1, rho_x: 1.00e-06
          acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
          nnz(A): 770, nnz(P): 571
------------------------------------------------------------------      
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.27e+02  9.56e+04  2.61e+06 -1.92e+04  1.00e-01  1.50e-03
    25| 1.07e-05  1.73e+01  1.92e+01 -4.92e-01  1.00e-01  1.78e-03
------------------------------------------------------------------
status:  infeasible
timings: total: 1.90e-03s = setup: 9.41e-04s + solve: 9.60e-04s
         lin-sys: 1.26e-04s, cones: 9.50e-06s, accel: 1.40e-06s
------------------------------------------------------------------
objective = inf
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = inf, SCSsol['info']['status'] = 'infeasible', SCSsol['x'] = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.47395779415549794, clarabel_solution.status = Solved, clarabel_solution.x = [0.0027904978965449666, 0.002510198944930076, 0.0022831938027705117, 0.0020628662164093115, 0.0018645011729976001, 0.0016845818065795019, 0.001518539175488385, 0.0013930633137464905, 0.0012596049961827414, 0.0006369214890962764, 0.0022030588661007553, 0.0025572221591200366, 0.0020148959955852168, 0.0011160350187950417, 7.508467158983145e-12, 5.098200436696795e-11, 5.0919837101523314e-11, 5.086396393312051e-11, 5.076849130648014e-11, 
5.0727771773585275e-11, 5.0691087613373826e-11, 5.06282096624894e-11, 5.060131621510247e-11, 5.057704866048211e-11, 5.069986578401336e-11, 5.116678636363365e-11, 5.302258154382985e-11, 5.873031052501555e-11, 6.015784957736915e-11, 6.028859646361538e-11, 5.858461187983256e-11, 5.749518217098852e-11, 5.6554606511962526e-11, 5.5554309438862856e-11, 5.550824040336608e-11, 5.5663838781453607e-11, 5.622236098014281e-11, 5.650877287399536e-11, 5.67251738200511e-11, 5.689231675566984e-11, 5.6871853878784136e-11, 5.681457007507406e-11, 5.6676452814081476e-11, 5.6626453715519324e-11, 5.659015006674711e-11]
=============SCS_DynamicLayer_20240625_124826_273466.pickle=============
------------------------------------------------------------------
               SCS v3.2.4 - Splitting Conic Solver
        (c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------
problem:  variables n: 45, constraints m: 120
cones:    l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07
          alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
          max_iters: 100000, normalize: 1, rho_x: 1.00e-06
          acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
          nnz(A): 770, nnz(P): 571
------------------------------------------------------------------
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.27e+02  9.55e+04  2.61e+06 -1.87e+04  1.00e-01  2.44e-03
    75| 1.59e-13  1.45e+00  2.50e-01 -2.95e-02  1.00e-01  2.98e-03
------------------------------------------------------------------
status:  infeasible
timings: total: 3.11e-03s = setup: 2.00e-03s + solve: 1.11e-03s
         lin-sys: 3.29e-04s, cones: 2.03e-05s, accel: 5.90e-06s
------------------------------------------------------------------
objective = inf
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = inf, SCSsol['info']['status'] = 'infeasible', SCSsol['x'] = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.029336830707231296, clarabel_solution.status = Solved, clarabel_solution.x = [0.0006942523340928437, 0.0006245211531611647, 0.0005680402154852255, 0.0005132247970703782, 0.0004638732525990768, 0.00041911069714191607, 0.0003778005965243507, 0.0003465831910797031, 0.00031337981165299804, 0.00015846105492833785, 0.0005481037104770503, 0.0006362167512226497, 0.0005012902690682797, 0.00027766073090962665, 7.422584398118871e-12, 4.9908818125428526e-11, 4.989439411164203e-11, 4.988135885249981e-11, 4.985892511625296e-11, 4.9849294577522e-11, 4.9840585958786774e-11, 4.982558652410234e-11, 4.9819142820566604e-11, 4.981331356680122e-11, 4.996139585601154e-11, 5.043061010730071e-11, 5.2267431264695483e-11, 5.791302606624232e-11, 5.933302998604015e-11, 5.947440433414254e-11, 5.7813882893553324e-11, 5.674740148239301e-11, 5.582688709371417e-11, 5.485106791023405e-11, 5.481192913018784e-11, 5.497105449305245e-11, 5.55308513300546e-11, 5.5818938404726124e-11, 5.603741130163767e-11, 5.620771956517265e-11, 5.618918130039591e-11, 5.61345389366892e-11, 5.6001086068957654e-11, 5.595235055766623e-11, 5.5916314191366856e-11]
=============SCS_DynamicLayer_20240625_124826_366924.pickle=============
------------------------------------------------------------------
               SCS v3.2.4 - Splitting Conic Solver
        (c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------
problem:  variables n: 45, constraints m: 120
cones:    l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07
          alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
          max_iters: 100000, normalize: 1, rho_x: 1.00e-06
          acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
          nnz(A): 770, nnz(P): 571
------------------------------------------------------------------
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.26e+02  9.55e+04  2.61e+06 -1.86e+04  1.00e-01  2.09e-03
    50| 3.46e-13  4.37e+00  1.68e+00 -2.37e-03  1.00e-01  2.46e-03
------------------------------------------------------------------
status:  infeasible
timings: total: 2.57e-03s = setup: 1.69e-03s + solve: 8.78e-04s
         lin-sys: 2.05e-04s, cones: 1.33e-05s, accel: 3.60e-06s
------------------------------------------------------------------
objective = inf
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = inf, SCSsol['info']['status'] = 'infeasible', SCSsol['x'] = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.0022152174267624503, clarabel_solution.status = Solved, clarabel_solution.x = [0.00019076221443803517, 0.00017161401041454087, 0.00015608744276640255, 0.00014102549607533437, 0.00012746475443084693, 0.00011516469875157536, 0.00010381329027656681, 9.523573477537405e-05, 8.611244900019338e-05, 4.353157804871756e-05, 0.0001506285695843726, 0.00017480015346372276, 0.0001378277079923815, 7.633724223309769e-05, 7.402155371667622e-12, 4.965827303819483e-11, 4.965437612207606e-11, 4.9650849634359917e-11, 4.9644769875527325e-11, 4.9642155753222594e-11, 4.963978972099507e-11, 4.963570972671434e-11, 4.9633955095898954e-11, 4.963236680374209e-11, 4.9786226003858936e-11, 5.025590754635561e-11, 5.2088158356424624e-11, 5.771888610298373e-11, 5.913705369744592e-11, 5.928091578082756e-11, 5.763067279392684e-11, 5.6569626886655146e-11, 5.565386489883837e-11, 5.4683841907138966e-11, 5.464633867216354e-11, 5.48062925637533e-11, 5.5366378165935806e-11, 5.5654855288171645e-11, 5.587381381126671e-11, 5.6044868611528313e-11, 5.602678592850865e-11, 5.5972769367126994e-11, 5.584042230149203e-11, 5.5791986607535523e-11, 5.575601417531867e-11]

bodono · 2024-06-26T16:09:29Z

Thanks for posting this! I still have not been able to reproduce this but I think I have identified the problem, a very nasty bug in how I'm calling the Blas dlange method, for calculating the infinity norm. I'm amazed this didn't trigger a bug before!

If you are able to test please can you patch in this change: 8891c9c

And then retry? I can talk you through how to do this if you're having trouble.

ymwang78 · 2024-06-27T02:35:01Z

build source from main branch (the change is not applied), seems no problem:

scs.version = '3.2.4'
clarabel.version = '0.9.0'
=============SCS_DynamicLayer_20240625_124826_169446.pickle=============
-----------SCS-----------
SCSsol['info']['pobj'] = -0.47395779415545064, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([2.79051286e-03, 2.51015842e-03, 2.28324254e-03, 2.06283394e-03,
1.86450946e-03, 1.68458889e-03, 1.51852915e-03, 1.39307048e-03,
1.25959953e-03, 6.36925612e-04, 2.20305700e-03, 2.55722225e-03,
2.01489587e-03, 1.11603498e-03, 8.84477770e-13, 2.44157244e-13,
2.44157344e-13, 2.44157046e-13, 2.44157365e-13, 2.44157298e-13,
2.44156987e-13, 2.44157257e-13, 2.44157240e-13, 2.44157125e-13,
2.43693110e-13, 2.42397618e-13, 2.39949448e-13, 2.32630732e-13,
2.28772681e-13, 2.25286026e-13, 2.19738649e-13, 2.17524842e-13,
2.15558016e-13, 2.13102962e-13, 2.12850018e-13, 2.12967835e-13,
2.13659155e-13, 2.13849878e-13, 2.13608392e-13, 2.12393270e-13,
2.11938506e-13, 2.11451457e-13, 2.09677605e-13, 2.08819459e-13,
2.08430287e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.47395779415549794, clarabel_solution.status = Solved, clarabel_solution.x = [0.0027904978965449666, 0.002510198944930076, 0.0022831938027705117, 0.0020628662164093115, 0.0018645011729976001, 0.0016845818065795019, 0.001518539175488385, 0.0013930633137464905, 0.0012596049961827414, 0.0006369214890962764, 0.0022030588661007553, 0.0025572221591200366, 0.0020148959955852168, 0.0011160350187950417, 7.508467158983145e-12, 5.098200436696795e-11, 5.0919837101523314e-11, 5.086396393312051e-11, 5.076849130648014e-11, 5.0727771773585275e-11, 5.0691087613373826e-11, 5.06282096624894e-11, 5.060131621510247e-11, 5.057704866048211e-11, 5.069986578401336e-11, 5.116678636363365e-11, 5.302258154382985e-11, 5.873031052501555e-11, 6.015784957736915e-11, 6.028859646361538e-11, 5.858461187983256e-11, 5.749518217098852e-11, 5.6554606511962526e-11, 5.5554309438862856e-11, 5.550824040336608e-11, 5.5663838781453607e-11, 5.622236098014281e-11, 5.650877287399536e-11, 5.67251738200511e-11, 5.689231675566984e-11, 5.6871853878784136e-11, 5.681457007507406e-11, 5.6676452814081476e-11, 5.6626453715519324e-11, 5.659015006674711e-11]
=============SCS_DynamicLayer_20240625_124826_273466.pickle=============
-----------SCS-----------
SCSsol['info']['pobj'] = -0.029336830707218327, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([6.94259936e-04, 6.24501827e-04, 5.68063419e-04, 5.13208238e-04,
4.63876894e-04, 4.19118079e-04, 3.77790690e-04, 3.46590442e-04,
3.13374274e-04, 1.58465238e-04, 5.48101857e-04, 6.36216882e-04,
5.01290173e-04, 2.77660712e-04, 8.99178393e-13, 2.44076312e-13,
2.44076201e-13, 2.44076130e-13, 2.44076233e-13, 2.44076309e-13,
2.44076333e-13, 2.44076175e-13, 2.44076303e-13, 2.44076117e-13,
2.43612146e-13, 2.42317150e-13, 2.39869209e-13, 2.32552922e-13,
2.28695235e-13, 2.25210428e-13, 2.19664045e-13, 2.17451237e-13,
2.15484886e-13, 2.13030754e-13, 2.12777663e-13, 2.12895197e-13,
2.13586552e-13, 2.13777236e-13, 2.13535904e-13, 2.12321086e-13,
2.11866553e-13, 2.11379857e-13, 2.09606355e-13, 2.08748669e-13,
2.08359173e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.029336830707231296, clarabel_solution.status = Solved, clarabel_solution.x = [0.0006942523340928437, 0.0006245211531611647, 0.0005680402154852255, 0.0005132247970703782, 0.0004638732525990768, 0.00041911069714191607, 0.0003778005965243507, 0.0003465831910797031, 0.00031337981165299804, 0.00015846105492833785, 0.0005481037104770503, 0.0006362167512226497, 0.0005012902690682797, 0.00027766073090962665, 7.422584398118871e-12, 4.9908818125428526e-11, 4.989439411164203e-11, 4.988135885249981e-11, 4.985892511625296e-11, 4.9849294577522e-11, 4.9840585958786774e-11, 4.982558652410234e-11, 4.9819142820566604e-11, 4.981331356680122e-11, 4.996139585601154e-11, 5.043061010730071e-11, 5.2267431264695483e-11, 5.791302606624232e-11, 5.933302998604015e-11, 5.947440433414254e-11, 5.7813882893553324e-11, 5.674740148239301e-11, 5.582688709371417e-11, 5.485106791023405e-11, 5.481192913018784e-11, 5.497105449305245e-11, 5.55308513300546e-11, 5.5818938404726124e-11, 5.603741130163767e-11, 5.620771956517265e-11, 5.618918130039591e-11, 5.61345389366892e-11, 5.6001086068957654e-11, 5.595235055766623e-11, 5.5916314191366856e-11]
=============SCS_DynamicLayer_20240625_124826_366924.pickle=============
-----------SCS-----------
SCSsol['info']['pobj'] = -0.002215217426752619, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([1.90768627e-04, 1.71597468e-04, 1.56107152e-04, 1.41012605e-04,
1.27465658e-04, 1.15173066e-04, 1.03803338e-04, 9.52429990e-05,
8.61068786e-05, 4.35357896e-05, 1.50626712e-04, 1.74800292e-04,
1.37827617e-04, 7.63372261e-05, 9.02991412e-13, 2.44051336e-13,
2.44051360e-13, 2.44051268e-13, 2.44051210e-13, 2.44051394e-13,
2.44051256e-13, 2.44051419e-13, 2.44051233e-13, 2.44051275e-13,
2.43587140e-13, 2.42292083e-13, 2.39844607e-13, 2.32528533e-13,
2.28671382e-13, 2.25186628e-13, 2.19641011e-13, 2.17428008e-13,
2.15461903e-13, 2.13008008e-13, 2.12755080e-13, 2.12872718e-13,
2.13563827e-13, 2.13754464e-13, 2.13512943e-13, 2.12298399e-13,
2.11844188e-13, 2.11356916e-13, 2.09583937e-13, 2.08726103e-13,
2.08336882e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.0022152174267624503, clarabel_solution.status = Solved, clarabel_solution.x = [0.00019076221443803517, 0.00017161401041454087, 0.00015608744276640255, 0.00014102549607533437, 0.00012746475443084693, 0.00011516469875157536, 0.00010381329027656681, 9.523573477537405e-05, 8.611244900019338e-05, 4.353157804871756e-05, 0.0001506285695843726, 0.00017480015346372276, 0.0001378277079923815, 7.633724223309769e-05, 7.402155371667622e-12, 4.965827303819483e-11, 4.965437612207606e-11, 4.9650849634359917e-11, 4.9644769875527325e-11, 4.9642155753222594e-11, 4.963978972099507e-11, 4.963570972671434e-11, 4.9633955095898954e-11, 4.963236680374209e-11, 4.9786226003858936e-11, 5.025590754635561e-11, 5.2088158356424624e-11, 5.771888610298373e-11, 5.913705369744592e-11, 5.928091578082756e-11, 5.763067279392684e-11, 5.6569626886655146e-11, 5.565386489883837e-11, 5.4683841907138966e-11, 5.464633867216354e-11, 5.48062925637533e-11, 5.5366378165935806e-11, 5.5654855288171645e-11, 5.587381381126671e-11, 5.6044868611528313e-11, 5.602678592850865e-11, 5.5972769367126994e-11, 5.584042230149203e-11, 5.5791986607535523e-11, 5.575601417531867e-11]

ymwang78 · 2024-06-27T02:46:59Z

After applied the patch, got the same result:

scs.version = '3.2.4'
clarabel.version = '0.9.0'
=============SCS_DynamicLayer_20240625_124826_169446.pickle=============
-----------SCS-----------
SCSsol['info']['pobj'] = -0.47395779415545064, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([2.79051286e-03, 2.51015842e-03, 2.28324254e-03, 2.06283394e-03,
1.86450946e-03, 1.68458889e-03, 1.51852915e-03, 1.39307048e-03,
1.25959953e-03, 6.36925612e-04, 2.20305700e-03, 2.55722225e-03,
2.01489587e-03, 1.11603498e-03, 8.84477770e-13, 2.44157244e-13,
2.44157344e-13, 2.44157046e-13, 2.44157365e-13, 2.44157298e-13,
2.44156987e-13, 2.44157257e-13, 2.44157240e-13, 2.44157125e-13,
2.43693110e-13, 2.42397618e-13, 2.39949448e-13, 2.32630732e-13,
2.28772681e-13, 2.25286026e-13, 2.19738649e-13, 2.17524842e-13,
2.15558016e-13, 2.13102962e-13, 2.12850018e-13, 2.12967835e-13,
2.13659155e-13, 2.13849878e-13, 2.13608392e-13, 2.12393270e-13,
2.11938506e-13, 2.11451457e-13, 2.09677605e-13, 2.08819459e-13,
2.08430287e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.47395779415549794, clarabel_solution.status = Solved, clarabel_solution.x = [0.0027904978965449666, 0.002510198944930076, 0.0022831938027705117, 0.0020628662164093115, 0.0018645011729976001, 0.0016845818065795019, 0.001518539175488385, 0.0013930633137464905, 0.0012596049961827414, 0.0006369214890962764, 0.0022030588661007553, 0.0025572221591200366, 0.0020148959955852168, 0.0011160350187950417, 7.508467158983145e-12, 5.098200436696795e-11, 5.0919837101523314e-11, 5.086396393312051e-11, 5.076849130648014e-11, 5.0727771773585275e-11, 5.0691087613373826e-11, 5.06282096624894e-11, 5.060131621510247e-11, 5.057704866048211e-11, 5.069986578401336e-11, 5.116678636363365e-11, 5.302258154382985e-11, 5.873031052501555e-11, 6.015784957736915e-11, 6.028859646361538e-11, 5.858461187983256e-11, 5.749518217098852e-11, 5.6554606511962526e-11, 5.5554309438862856e-11, 5.550824040336608e-11, 5.5663838781453607e-11, 5.622236098014281e-11, 5.650877287399536e-11, 5.67251738200511e-11, 5.689231675566984e-11, 5.6871853878784136e-11, 5.681457007507406e-11, 5.6676452814081476e-11, 5.6626453715519324e-11, 5.659015006674711e-11]
=============SCS_DynamicLayer_20240625_124826_273466.pickle=============
-----------SCS-----------
SCSsol['info']['pobj'] = -0.029336830707218327, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([6.94259936e-04, 6.24501827e-04, 5.68063419e-04, 5.13208238e-04,
4.63876894e-04, 4.19118079e-04, 3.77790690e-04, 3.46590442e-04,
3.13374274e-04, 1.58465238e-04, 5.48101857e-04, 6.36216882e-04,
5.01290173e-04, 2.77660712e-04, 8.99178393e-13, 2.44076312e-13,
2.44076201e-13, 2.44076130e-13, 2.44076233e-13, 2.44076309e-13,
2.44076333e-13, 2.44076175e-13, 2.44076303e-13, 2.44076117e-13,
2.43612146e-13, 2.42317150e-13, 2.39869209e-13, 2.32552922e-13,
2.28695235e-13, 2.25210428e-13, 2.19664045e-13, 2.17451237e-13,
2.15484886e-13, 2.13030754e-13, 2.12777663e-13, 2.12895197e-13,
2.13586552e-13, 2.13777236e-13, 2.13535904e-13, 2.12321086e-13,
2.11866553e-13, 2.11379857e-13, 2.09606355e-13, 2.08748669e-13,
2.08359173e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.029336830707231296, clarabel_solution.status = Solved, clarabel_solution.x = [0.0006942523340928437, 0.0006245211531611647, 0.0005680402154852255, 0.0005132247970703782, 0.0004638732525990768, 0.00041911069714191607, 0.0003778005965243507, 0.0003465831910797031, 0.00031337981165299804, 0.00015846105492833785, 0.0005481037104770503, 0.0006362167512226497, 0.0005012902690682797, 0.00027766073090962665, 7.422584398118871e-12, 4.9908818125428526e-11, 4.989439411164203e-11, 4.988135885249981e-11, 4.985892511625296e-11, 4.9849294577522e-11, 4.9840585958786774e-11, 4.982558652410234e-11, 4.9819142820566604e-11, 4.981331356680122e-11, 4.996139585601154e-11, 5.043061010730071e-11, 5.2267431264695483e-11, 5.791302606624232e-11, 5.933302998604015e-11, 5.947440433414254e-11, 5.7813882893553324e-11, 5.674740148239301e-11, 5.582688709371417e-11, 5.485106791023405e-11, 5.481192913018784e-11, 5.497105449305245e-11, 5.55308513300546e-11, 5.5818938404726124e-11, 5.603741130163767e-11, 5.620771956517265e-11, 5.618918130039591e-11, 5.61345389366892e-11, 5.6001086068957654e-11, 5.595235055766623e-11, 5.5916314191366856e-11]
=============SCS_DynamicLayer_20240625_124826_366924.pickle=============
-----------SCS-----------
SCSsol['info']['pobj'] = -0.002215217426752619, SCSsol['info']['status'] = 'solved', SCSsol['x'] = array([1.90768627e-04, 1.71597468e-04, 1.56107152e-04, 1.41012605e-04,
1.27465658e-04, 1.15173066e-04, 1.03803338e-04, 9.52429990e-05,
8.61068786e-05, 4.35357896e-05, 1.50626712e-04, 1.74800292e-04,
1.37827617e-04, 7.63372261e-05, 9.02991412e-13, 2.44051336e-13,
2.44051360e-13, 2.44051268e-13, 2.44051210e-13, 2.44051394e-13,
2.44051256e-13, 2.44051419e-13, 2.44051233e-13, 2.44051275e-13,
2.43587140e-13, 2.42292083e-13, 2.39844607e-13, 2.32528533e-13,
2.28671382e-13, 2.25186628e-13, 2.19641011e-13, 2.17428008e-13,
2.15461903e-13, 2.13008008e-13, 2.12755080e-13, 2.12872718e-13,
2.13563827e-13, 2.13754464e-13, 2.13512943e-13, 2.12298399e-13,
2.11844188e-13, 2.11356916e-13, 2.09583937e-13, 2.08726103e-13,
2.08336882e-13])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.0022152174267624503, clarabel_solution.status = Solved, clarabel_solution.x = [0.00019076221443803517, 0.00017161401041454087, 0.00015608744276640255, 0.00014102549607533437, 0.00012746475443084693, 0.00011516469875157536, 0.00010381329027656681, 9.523573477537405e-05, 8.611244900019338e-05, 4.353157804871756e-05, 0.0001506285695843726, 0.00017480015346372276, 0.0001378277079923815, 7.633724223309769e-05, 7.402155371667622e-12, 4.965827303819483e-11, 4.965437612207606e-11, 4.9650849634359917e-11, 4.9644769875527325e-11, 4.9642155753222594e-11, 4.963978972099507e-11, 4.963570972671434e-11, 4.9633955095898954e-11, 4.963236680374209e-11, 4.9786226003858936e-11, 5.025590754635561e-11, 5.2088158356424624e-11, 5.771888610298373e-11, 5.913705369744592e-11, 5.928091578082756e-11, 5.763067279392684e-11, 5.6569626886655146e-11, 5.565386489883837e-11, 5.4683841907138966e-11, 5.464633867216354e-11, 5.48062925637533e-11, 5.5366378165935806e-11, 5.5654855288171645e-11, 5.587381381126671e-11, 5.6044868611528313e-11, 5.602678592850865e-11, 5.5972769367126994e-11, 5.584042230149203e-11, 5.5791986607535523e-11, 5.575601417531867e-11]

kkzhang3 · 2024-06-27T03:31:03Z

I asked my colleague @ymwang78, who is much more knowledgeable in C and C++ than I am, to help me with this issue. We discovered that if we install SCS through pip, we both encounter the same infeasibility error. However, when we build SCS from the original source code, we get the correct results. This observation holds true for both the main branch and the new modified branch 8891c9c.

We use python 3.12 on windows 11 system.

bodono · 2024-06-27T16:15:29Z

Thanks for all the information. I can only think of the difference when installing via pip is from linking against a different blas library, but I'm surprised that the change didn't fix it. I am planning to merge that fix and cut a new pip release with it, so it should be easy to just use pip install again to pick up the new version and we can test again then. This is very mysterious!

bodono · 2024-06-27T17:35:17Z

Ok i just cut a new pip release, can you pip install or pip upgrade and retry? Make sure it's version 3.2.5. Fingers crossed this fixes it.

kkzhang3 · 2024-06-28T00:33:32Z

I tried version 3.2.5 by installing it via pip. To my surprise, the infeasibility issue still persists.

scs.__version__ = '3.2.5'
clarabel.__version__ = '0.7.1'
=============SCS_DynamicLayer_20240625_124826_169446.pickle=============
------------------------------------------------------------------      
               SCS v3.2.5 - Splitting Conic Solver
        (c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------      
problem:  variables n: 45, constraints m: 120
cones:    l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07       
          alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
          max_iters: 100000, normalize: 1, rho_x: 1.00e-06
          acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
          nnz(A): 770, nnz(P): 571
------------------------------------------------------------------
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.27e+02  9.56e+04  2.61e+06 -1.92e+04  1.00e-01  2.36e-03
    25| 1.07e-05  1.73e+01  1.92e+01 -4.92e-01  1.00e-01  2.63e-03
------------------------------------------------------------------
status:  infeasible
timings: total: 2.76e-03s = setup: 1.17e-03s + solve: 1.59e-03s
         lin-sys: 1.04e-04s, cones: 1.09e-05s, accel: 1.60e-06s
------------------------------------------------------------------
objective = inf
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = inf, SCSsol['info']['status'] = 'infeasible', SCSsol['x'] = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.47395779415549794, clarabel_solution.status = Solved, clarabel_solution.x = [0.0027904978965449666, 0.002510198944930076, 0.0022831938027705117, 0.0020628662164093115, 0.0018645011729976001, 0.0016845818065795019, 0.001518539175488385, 0.0013930633137464905, 0.0012596049961827414, 0.0006369214890962764, 0.0022030588661007553, 0.0025572221591200366, 0.0020148959955852168, 0.0011160350187950417, 7.508467158983145e-12, 5.098200436696795e-11, 5.0919837101523314e-11, 5.086396393312051e-11, 5.076849130648014e-11, 5.0727771773585275e-11, 5.0691087613373826e-11, 5.06282096624894e-11, 5.060131621510247e-11, 5.057704866048211e-11, 
5.069986578401336e-11, 5.116678636363365e-11, 5.302258154382985e-11, 5.873031052501555e-11, 6.015784957736915e-11, 6.028859646361538e-11, 5.858461187983256e-11, 5.749518217098852e-11, 5.6554606511962526e-11, 5.5554309438862856e-11, 5.550824040336608e-11, 5.5663838781453607e-11, 5.622236098014281e-11, 5.650877287399536e-11, 5.67251738200511e-11, 5.689231675566984e-11, 5.6871853878784136e-11, 5.681457007507406e-11, 5.6676452814081476e-11, 5.6626453715519324e-11, 5.659015006674711e-11]
=============SCS_DynamicLayer_20240625_124826_273466.pickle=============
------------------------------------------------------------------
               SCS v3.2.5 - Splitting Conic Solver
        (c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------
problem:  variables n: 45, constraints m: 120
cones:    l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07
          alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
          max_iters: 100000, normalize: 1, rho_x: 1.00e-06
          acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
          nnz(A): 770, nnz(P): 571
------------------------------------------------------------------
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.27e+02  9.55e+04  2.61e+06 -1.87e+04  1.00e-01  3.44e-03
    75| 1.59e-13  1.45e+00  2.50e-01 -2.95e-02  1.00e-01  4.17e-03
------------------------------------------------------------------
status:  infeasible
timings: total: 4.30e-03s = setup: 2.76e-03s + solve: 1.54e-03s
         lin-sys: 4.51e-04s, cones: 3.50e-05s, accel: 1.15e-05s
------------------------------------------------------------------
objective = inf
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = inf, SCSsol['info']['status'] = 'infeasible', SCSsol['x'] = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.029336830707231296, clarabel_solution.status = Solved, clarabel_solution.x = [0.0006942523340928437, 0.0006245211531611647, 0.0005680402154852255, 0.0005132247970703782, 0.0004638732525990768, 0.00041911069714191607, 0.0003778005965243507, 0.0003465831910797031, 0.00031337981165299804, 0.00015846105492833785, 0.0005481037104770503, 0.0006362167512226497, 0.0005012902690682797, 0.00027766073090962665, 7.422584398118871e-12, 4.9908818125428526e-11, 4.989439411164203e-11, 4.988135885249981e-11, 4.985892511625296e-11, 4.9849294577522e-11, 4.9840585958786774e-11, 4.982558652410234e-11, 4.9819142820566604e-11, 4.981331356680122e-11, 4.996139585601154e-11, 5.043061010730071e-11, 5.2267431264695483e-11, 5.791302606624232e-11, 5.933302998604015e-11, 5.947440433414254e-11, 5.7813882893553324e-11, 5.674740148239301e-11, 5.582688709371417e-11, 5.485106791023405e-11, 5.481192913018784e-11, 5.497105449305245e-11, 5.55308513300546e-11, 5.5818938404726124e-11, 5.603741130163767e-11, 5.620771956517265e-11, 5.618918130039591e-11, 5.61345389366892e-11, 5.6001086068957654e-11, 5.595235055766623e-11, 5.5916314191366856e-11]
=============SCS_DynamicLayer_20240625_124826_366924.pickle=============
------------------------------------------------------------------
               SCS v3.2.5 - Splitting Conic Solver
        (c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------
problem:  variables n: 45, constraints m: 120
cones:    l: linear vars: 120
settings: eps_abs: 1.0e-07, eps_rel: 1.0e-06, eps_infeas: 1.0e-07
          alpha: 1.50, scale: 1.00e-01, adaptive_scale: 1
          max_iters: 100000, normalize: 1, rho_x: 1.00e-06
          acceleration_lookback: 10, acceleration_interval: 10
lin-sys:  sparse-direct-amd-qdldl
          nnz(A): 770, nnz(P): 571
------------------------------------------------------------------
 iter | pri res | dua res |   gap   |   obj   |  scale  | time (s)
------------------------------------------------------------------
     0| 2.26e+02  9.55e+04  2.61e+06 -1.86e+04  1.00e-01  3.43e-03
    50| 3.46e-13  4.37e+00  1.68e+00 -2.37e-03  1.00e-01  3.83e-03
------------------------------------------------------------------
status:  infeasible
timings: total: 3.94e-03s = setup: 2.63e-03s + solve: 1.32e-03s
         lin-sys: 2.17e-04s, cones: 1.64e-05s, accel: 5.50e-06s
------------------------------------------------------------------
objective = inf
------------------------------------------------------------------
-----------SCS-----------
SCSsol['info']['pobj'] = inf, SCSsol['info']['status'] = 'infeasible', SCSsol['x'] = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan])
-----------CLARABEL-----------
clarabel_solution.obj_val = -0.0022152174267624503, clarabel_solution.status = Solved, clarabel_solution.x = [0.00019076221443803517, 0.00017161401041454087, 0.00015608744276640255, 0.00014102549607533437, 0.00012746475443084693, 0.00011516469875157536, 0.00010381329027656681, 9.523573477537405e-05, 8.611244900019338e-05, 4.353157804871756e-05, 0.0001506285695843726, 0.00017480015346372276, 0.0001378277079923815, 7.633724223309769e-05, 
7.402155371667622e-12, 4.965827303819483e-11, 4.965437612207606e-11, 4.9650849634359917e-11, 4.9644769875527325e-11, 4.9642155753222594e-11, 4.963978972099507e-11, 4.963570972671434e-11, 4.9633955095898954e-11, 4.963236680374209e-11, 4.9786226003858936e-11, 5.025590754635561e-11, 5.2088158356424624e-11, 5.771888610298373e-11, 5.913705369744592e-11, 5.928091578082756e-11, 5.763067279392684e-11, 5.6569626886655146e-11, 5.565386489883837e-11, 5.4683841907138966e-11, 5.464633867216354e-11, 5.48062925637533e-11, 5.5366378165935806e-11, 5.5654855288171645e-11, 5.587381381126671e-11, 5.6044868611528313e-11, 5.602678592850865e-11, 5.5972769367126994e-11, 5.584042230149203e-11, 5.5791986607535523e-11, 5.575601417531867e-11]

bodono · 2024-06-28T00:35:40Z

Oh no! Can you dump the data down to csv again for one of them?

kkzhang3 · 2024-06-28T00:45:44Z

scs_err_csv.zip

bodono · 2024-06-28T14:13:08Z

Ok I have been able to reproduce this, as far as I can tell it is just numerical problems when the values are very small producing inconsistent results between different hardware. I will try and improve the robustness of the infeasibility checks by ensuring the things being compared are at some minimum value.

kkzhang3 · 2024-06-29T00:20:41Z

Oh, Thanks! Looking forward to the new version! I can provide more unexcepct infeasible examples. Let me know if it is necessary.

bodono · 2024-07-02T05:00:43Z

I believe I have fixed the issue in this PR: #280

I added the data files you included as tests, is this ok? If not I can remove them.

kkzhang3 · 2024-07-02T05:03:27Z

It's OK. I'll try the new version later.

kkzhang3 · 2024-07-03T10:09:42Z

Could you update a pip release?

bodono · 2024-07-05T13:44:44Z

Yes, will do as soon as I can figure out what's wrong with bodono/scs-python#103

bodono · 2024-07-07T11:28:36Z

Ok, it's on pip now.

kkzhang3 · 2024-07-09T07:05:27Z

Thanks. I tried. It works great.

bodono · 2024-07-09T14:51:53Z

Glad to hear it!

bodono closed this as completed Jul 9, 2024

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Unexpected 'Infeasible' Results with SCS 3.2.4 on QP Problems for MPC #275

Unexpected 'Infeasible' Results with SCS 3.2.4 on QP Problems for MPC #275

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