Improve getting started instructions

[adowling2]

I am using this issue to take notes as I test the installation/getting started instructions.

[adowling2]

It is not clear that Steps 2 to 5 are options within kinetics_MO.py.

[adowling2]

For Step 2, it is unclear what are the default options to rerun that paper.

[adowling2]

Recommendation: within kinetics_MO.py, define a function that runs the workflow. Then offer a few options for calling the function to reproduce different results from the paper.

[adowling2]

I successfully ran kinetics_MO.py and rotary_bed_MO.py with default settings for A-optimality. I am still debugging cyipopt executable not installing.

Cyipopt debugging: mechmotum/cyipopt#245

[adowling2]

Switched order of installation instructions. Installed cyipopt immediately after creating the environmental. However, Gurobi does not support Python 3.8.

[adowling2]

I added details on installed HSL on macOS. Unfortunately, I am now getting a seg fault:

====Solving with budget: 15000 ====
using solution at 15000.0  to initialize, file name: ./rotary_results/LP_15000_a
if_install_dynamic is initialized to be: [0.02, 0.2, 0, 0, 0.2]
Initialize with solution: [[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]]
warmstart FIM computed by integer decisions: [[ 1.16593803e+04  3.33936694e+02 -4.46528109e+03  2.54113819e+04
  -9.38076899e+03]
 [ 3.33936694e+02  2.53764585e+01 -1.90026909e+01  8.92193875e+02
  -5.05942795e+02]
 [-4.46528109e+03 -1.90026909e+01  3.49559782e+03 -9.39405006e+03
   5.78208268e+02]
 [ 2.54113819e+04  8.92193875e+02 -9.39405006e+03  6.21724852e+04
  -2.16288454e+04]
 [-9.38076899e+03 -5.05942795e+02  5.78208268e+02 -2.16288454e+04
   1.35294336e+04]]
Warmstart initialize FIM with:  [[ 1.16593804e+04  3.33936694e+02 -4.46528109e+03  2.54113819e+04
  -9.38076899e+03]
 [ 3.33936694e+02  2.53765585e+01 -1.90026909e+01  8.92193875e+02
  -5.05942795e+02]
 [-4.46528109e+03 -1.90026909e+01  3.49559792e+03 -9.39405006e+03
   5.78208268e+02]
 [ 2.54113819e+04  8.92193875e+02 -9.39405006e+03  6.21724853e+04
  -2.16288454e+04]
 [-9.38076899e+03 -5.05942795e+02  5.78208268e+02 -2.16288454e+04
   1.35294337e+04]]
Warmstart logdet: 33.81882302917844
Warmstart eigen value: [8.22667569e+04 7.00705901e+03 1.46466389e+03 1.39666155e+02
 4.12799272e+00]
warmstart initialize total measure: 18.979999999999958
warmstart initialize total dynamic:  10.0
warmstart initialize cost: 14982.0
This is Ipopt version 3.14.14, running with linear solver ma57.

Number of nonzeros in equality constraint Jacobian...:    10136
Number of nonzeros in inequality constraint Jacobian.:  1104472
Number of nonzeros in Lagrangian Hessian.............:        0

Total number of variables............................:   157680
                     variables with only lower bounds:        0
                variables with lower and upper bounds:   157646
                     variables with only upper bounds:        0
Total number of equality constraints.................:       34
Total number of inequality constraints...............:   471915
        inequality constraints with only lower bounds:        1
   inequality constraints with lower and upper bounds:        0
        inequality constraints with only upper bounds:   471914

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
   0 -3.3818823e+01 2.13e-11 4.50e+01   0.0 0.00e+00    -  0.00e+00 0.00e+00   0
   1 -3.3818823e+01 2.26e-11 4.50e+01  -6.4 4.20e-02    -  1.63e-04 2.28e-02f  1
   2 -3.3845898e+01 2.15e-03 7.02e+04  -0.5 1.22e+05    -  3.74e-08 4.10e-03f  1
   3 -3.3844261e+01 1.04e-07 1.95e+00  -6.4 6.47e-01    -  9.57e-01 1.00e+00h  1
   4 -3.3860847e+01 1.09e-04 2.81e-02  -7.8 1.92e+01    -  4.80e-01 1.00e+00f  1
   5 -3.3951471e+01 3.30e-03 2.78e-02  -8.0 1.23e+02    -  2.23e-01 9.15e-01f  1
   6 -3.4072904e+01 7.96e-03 2.80e-02  -8.2 4.31e+02    -  2.07e-01 3.86e-01f  1
   7 -3.4294118e+01 2.28e-02 2.78e-02  -8.3 6.63e+02    -  2.55e-01 5.12e-01f  1
   8 -3.4785983e+01 8.80e-02 1.59e-02  -8.4 1.02e+03    -  3.26e-01 9.02e-01f  1
   9 -3.5032117e+01 7.51e-02 1.56e-02  -8.6 1.50e+03    -  4.33e-01 4.97e-01f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  10 -3.5246692e+01 7.18e-02 1.36e-02  -8.8 2.24e+03    -  2.30e-01 3.44e-01f  1
  11 -3.5451752e+01 6.88e-02 1.14e-02  -8.9 2.57e+03    -  2.72e-01 3.51e-01f  1
  12 -3.5618808e+01 6.34e-02 1.36e-02  -9.1 2.79e+03    -  1.08e-01 3.25e-01f  1
  13 -3.5759814e+01 5.64e-02 8.40e-03  -9.1 2.78e+03    -  3.50e-01 3.35e-01h  1
  14 -3.5929765e+01 5.40e-02 8.40e-03  -9.3 2.86e+03    -  3.50e-01 4.73e-01f  1
  15 -3.6030622e+01 4.43e-02 7.82e-03  -9.5 2.55e+03    -  3.06e-01 4.27e-01h  1
  16 -3.6069293e+01 3.72e-02 9.73e-03  -9.6 2.14e+03    -  5.93e-02 2.16e-01h  1
  17 -3.6080861e+01 3.48e-02 1.09e-02  -5.8 1.95e+03    -  5.17e-02 7.16e-02f  1
  18 -3.6083383e+01 3.43e-02 5.99e-03  -5.3 1.90e+03    -  5.04e-02 1.40e-02f  1
  19 -3.6092553e+01 3.30e-02 9.79e-03  -5.4 1.70e+03    -  1.04e-01 3.93e-02f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  20 -3.6141157e+01 2.96e-02 6.07e-03  -5.6 1.31e+03    -  3.12e-02 1.43e-01f  1
  21 -3.6268691e+01 2.64e-02 8.77e-03  -9.8 2.61e+03    -  7.71e-02 3.13e-01f  1
  22 -3.6281977e+01 2.55e-02 6.83e-03  -9.8 3.29e+03    -  1.53e-01 3.70e-02h  1
  23 -3.6294960e+01 2.46e-02 1.12e-02  -5.8 3.83e+03    -  1.38e-01 3.50e-02f  1
  24 -3.6404524e+01 2.09e-02 4.87e-03  -5.6 4.10e+03    -  1.70e-01 2.95e-01f  1
  25 -3.6482753e+01 1.82e-02 4.21e-03  -5.9 4.73e+03    -  1.63e-01 2.10e-01f  1
  26 -3.6484720e+01 1.80e-02 9.37e-02  -5.6 3.45e+03    -  2.41e-01 8.62e-03f  1
  27 -3.6498829e+01 1.69e-02 1.26e-01  -5.7 3.43e+03    -  1.27e-01 6.70e-02f  1
  28 -3.6504547e+01 1.65e-02 1.75e-01  -6.1 4.48e+03    -  7.91e-02 2.42e-02h  1
  29 -3.6538520e+01 1.50e-02 2.48e-01  -5.8 6.29e+03    -  3.19e-01 1.14e-01f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  30 -3.6561133e+01 1.42e-02 2.04e-01 -10.5 7.30e+03    -  2.80e-02 6.90e-02h  1
  31 -3.6602248e+01 1.27e-02 1.77e-01  -5.7 5.63e+03    -  2.00e-01 1.32e-01f  1
  32 -3.6617105e+01 1.22e-02 1.59e-01 -10.6 6.76e+03    -  3.12e-02 4.74e-02h  1
  33 -3.6629662e+01 1.17e-02 1.70e-01  -5.8 5.12e+03    -  2.01e-01 4.50e-02f  1
  34 -3.6650992e+01 1.09e-02 1.57e-01  -5.9 5.25e+03    -  7.49e-02 7.58e-02f  1
  35 -3.6667932e+01 1.02e-02 1.43e-01  -5.7 3.89e+03    -  1.72e-01 7.29e-02f  1
  36 -3.6688518e+01 9.29e-03 1.41e-01  -5.6 2.28e+03    -  5.15e-02 1.15e-01f  1
  37 -3.6722833e+01 8.32e-03 1.31e-01  -6.2 4.92e+03    -  1.80e-01 1.43e-01f  1
  38 -3.6748845e+01 7.69e-03 1.19e-01  -7.0 5.00e+03    -  1.07e-01 1.03e-01h  1
  39 -3.6752257e+01 7.57e-03 1.24e-01  -6.4 4.19e+03    -  5.35e-02 1.56e-02h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  40 -3.6777561e+01 6.62e-03 1.01e-01  -6.0 3.23e+03    -  5.63e-02 1.45e-01f  1
  41 -3.6787067e+01 6.19e-03 9.57e-02  -6.0 2.87e+03    -  9.70e-02 6.83e-02h  1
  42 -3.6784095e+01 6.09e-03 8.59e-02  -5.3 1.08e+03    -  5.23e-02 1.81e-02f  1
  43 -3.6774140e+01 5.76e-03 8.80e-02  -5.3 9.54e+02    -  4.90e-02 7.67e-02f  1
  44 -3.6773398e+01 5.30e-03 8.49e-02  -5.5 1.46e+03    -  5.75e-02 8.94e-02f  1
  45 -3.6776133e+01 4.88e-03 8.13e-02  -5.5 1.61e+03    -  4.64e-02 8.42e-02f  1
  46 -3.6781596e+01 4.76e-03 8.69e-02 -11.0 3.20e+03    -  7.96e-02 2.57e-02h  1
  47 -3.6792864e+01 4.54e-03 7.94e-02 -11.0 2.98e+03    -  3.34e-02 5.27e-02h  1
  48 -3.6826431e+01 3.54e-03 5.87e-02  -5.9 1.89e+03    -  3.51e-01 2.67e-01f  1
  49 -3.6841146e+01 2.96e-03 4.81e-02  -5.9 1.34e+03    -  2.44e-01 1.75e-01h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  50 -3.6829415e+01 2.49e-03 2.37e-02  -5.5 5.28e+02    -  3.12e-01 2.07e-01f  1
  51 -3.6837186e+01 2.14e-03 4.68e-02  -5.8 1.07e+03    -  6.73e-01 1.47e-01h  1
  52 -3.6838947e+01 1.59e-03 3.42e-02  -5.6 6.16e+02    -  3.31e-01 2.63e-01h  1
  53 -3.6851857e+01 1.19e-03 6.75e-02  -5.8 7.86e+02    -  1.00e+00 2.81e-01h  1
  54 -3.6867212e+01 6.89e-04 3.60e-02  -5.8 5.65e+02    -  7.13e-01 4.71e-01h  1
  55 -3.6876809e+01 5.80e-04 5.41e-02  -5.9 5.40e+02    -  1.00e+00 1.92e-01h  1
  56 -3.6896907e+01 4.11e-04 2.77e-02  -5.9 4.18e+02    -  9.10e-01 4.68e-01h  1
  57 -3.6909294e+01 2.24e-04 1.20e-02  -5.9 2.52e+02    -  1.00e+00 5.55e-01h  1
  58 -3.6915858e+01 1.88e-04 2.25e-02  -6.0 2.41e+02    -  1.00e+00 2.14e-01h  1
  59 -3.6929555e+01 1.69e-04 1.84e-02  -6.1 3.00e+02    -  1.00e+00 3.70e-01h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  60 -3.6939794e+01 1.26e-04 1.10e-02  -6.1 1.92e+02    -  1.00e+00 4.25e-01h  1
  61 -3.6957664e+01 1.30e-04 6.47e-03  -6.1 2.14e+02    -  1.00e+00 6.73e-01h  1
  62 -3.6968748e+01 8.41e-05 4.60e-03  -6.2 1.37e+02    -  1.00e+00 6.19e-01h  1
  63 -3.6983915e+01 6.53e-05 2.73e-05  -6.2 1.21e+02    -  1.00e+00 1.00e+00h  1
  64 -3.6988899e+01 6.89e-06 2.64e-03  -6.2 3.61e+01    -  4.14e-02 1.00e+00h  1
  65 -3.6994274e+01 6.14e-06 4.06e-05  -6.2 6.29e+01    -  1.00e+00 1.00e+00h  1
  66 -3.7022601e+01 1.81e-04 9.47e-03  -6.3 2.76e+02    -  2.59e-01 9.86e-01f  1
  67 -3.7010857e+01 3.05e-05 1.13e-05  -6.3 1.12e+02    -  1.00e+00 1.00e+00h  1
  68 -3.7038908e+01 1.77e-04 9.25e-03  -6.4 2.83e+02    -  1.30e-01 9.90e-01h  1
  69 -3.7030873e+01 1.50e-05 4.92e-05  -6.4 9.37e+01    -  1.00e+00 1.00e+00h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  70 -3.7037023e+01 1.87e-05 4.57e-03  -6.4 1.65e+02    -  1.00e+00 3.28e-01h  1
  71 -3.7044990e+01 1.50e-05 2.45e-04  -6.4 8.82e+01    -  8.99e-01 1.00e+00h  1
  72 -3.7050093e+01 7.65e-06 1.28e-04  -6.4 2.66e+01    -  1.00e+00 1.00e+00h  1
  73 -3.7058382e+01 1.82e-05 3.50e-04  -6.5 1.22e+02    -  9.28e-01 7.90e-01h  1
  74 -3.7061884e+01 4.83e-06 1.14e-04  -6.5 9.99e+00    -  1.00e+00 1.00e+00h  1
  75 -3.7074106e+01 3.38e-05 1.16e-03  -6.5 1.22e+02    -  7.66e-01 9.69e-01h  1
  76 -3.7082918e+01 1.84e-05 2.71e-05  -6.6 6.92e+01    -  1.00e+00 1.00e+00h  1
  77 -3.7090509e+01 2.58e-05 2.08e-03  -6.7 2.09e+02    -  6.02e-01 3.13e-01h  1
  78 -3.7100432e+01 2.65e-05 5.12e-04  -6.6 1.02e+02    -  9.98e-01 8.49e-01h  1
  79 -3.7110124e+01 2.20e-05 3.52e-05  -6.7 7.90e+01    -  9.82e-01 1.00e+00h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  80 -3.7118017e+01 1.48e-05 1.85e-03  -6.7 7.05e+01    -  2.25e-01 9.87e-01h  1
  81 -3.7116393e+01 1.32e-06 2.18e-05  -6.7 4.41e+00    -  1.00e+00 1.00e+00f  1
  82 -3.7129737e+01 4.19e-05 3.58e-03 -11.0 6.68e+02    -  7.64e-02 1.93e-01f  1
  83 -3.7131135e+01 2.79e-05 2.72e-04  -6.9 3.64e+01    -  1.00e+00 3.47e-01h  1
  84 -3.7135788e+01 1.79e-05 1.07e-03  -6.9 1.00e+02    -  1.00e+00 5.38e-01h  1
  85 -3.7141231e+01 7.51e-06 2.35e-05  -6.9 5.08e+01    -  1.00e+00 9.85e-01h  1
  86 -3.7145497e+01 4.27e-06 1.48e-04  -7.0 4.98e+01    -  9.04e-01 1.00e+00h  1
  87 -3.7152963e+01 1.30e-05 2.26e-05  -7.0 7.52e+01    -  9.91e-01 1.00e+00h  1
  88 -3.7156776e+01 5.93e-06 3.18e-05  -7.1 5.99e+01    -  8.23e-01 8.00e-01h  1
  89 -3.7157549e+01 2.21e-07 6.44e-07  -7.1 2.05e+00    -  1.00e+00 1.00e+00h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  90 -3.7161679e+01 4.38e-06 1.25e-03  -7.3 1.33e+02    -  1.48e-01 4.10e-01f  1
  91 -3.7162530e+01 8.07e-07 3.03e-07  -7.2 6.70e+00    -  9.97e-01 8.78e-01f  1
  92 -3.7169314e+01 1.23e-05 1.54e-03 -11.0 3.53e+02    -  4.32e-02 2.53e-01f  1
  93 -3.7169031e+01 6.47e-06 2.82e-04  -7.3 1.68e+01    -  1.00e+00 4.79e-01f  2
  94 -3.7169255e+01 6.51e-08 1.53e-07  -7.3 2.09e+00    -  9.92e-01 1.00e+00h  1
  95 -3.7169996e+01 2.05e-07 1.16e-06 -11.0 2.18e+02    -  9.48e-02 3.66e-02f  1
  96 -3.7172591e+01 1.64e-06 2.63e-04  -7.4 3.10e+01    -  5.03e-01 1.00e+00f  1
  97 -3.7174416e+01 2.03e-06 2.31e-03  -7.6 8.26e+01    -  1.00e+00 2.62e-01h  1
  98 -3.7175916e+01 1.34e-06 9.57e-04  -7.5 3.05e+01    -  5.66e-01 6.04e-01h  1
  99 -3.7179234e+01 3.67e-06 1.05e-03  -7.8 6.85e+01    -  1.00e+00 4.29e-01f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
 100 -3.7182530e+01 3.44e-06 1.13e-05  -7.8 2.05e+01    -  1.00e+00 1.00e+00h  1
 101 -3.7183079e+01 9.40e-08 8.09e-08  -7.8 4.55e+00    -  9.28e-01 1.00e+00h  1
 102 -3.7183756e+01 2.03e-07 3.25e-07 -11.0 7.83e+01    -  7.49e-02 1.03e-01f  1
 103 -3.7187358e+01 5.28e-06 1.87e-03  -8.3 1.29e+01    -  1.01e-01 9.97e-01f  1
 104 -3.7187214e+01 4.31e-06 1.43e-03  -8.1 4.21e+00    -  1.00e+00 1.86e-01f  1
 105 -3.7187207e+01 7.60e-09 1.10e-07  -8.2 5.02e-01    -  1.00e+00 1.00e+00h  1
 106 -3.7187994e+01 3.10e-07 9.52e-07 -11.0 6.25e+00    -  1.26e-01 3.19e-01f  1
 107 -3.7188445e+01 1.30e-07 2.14e-07  -8.5 5.32e-01    -  9.03e-01 1.00e+00f  1
 108 -3.7188935e+01 1.11e-07 7.85e-07  -8.9 5.03e+00    -  9.91e-01 6.60e-01h  1
 109 -3.7189205e+01 1.91e-08 2.86e-07  -8.9 3.43e+00    -  6.06e-01 1.00e+00h  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
 110 -3.7189575e+01 3.51e-08 7.07e-08 -10.0 4.83e+00    -  9.97e-01 8.38e-01f  1
 111 -3.7189683e+01 2.69e-09 2.36e-09 -10.9 1.14e+00    -  9.79e-01 1.00e+00h  1

Number of Iterations....: 111

                                   (scaled)                 (unscaled)
Objective...............:  -3.7189683027570737e+01   -3.7189683027570737e+01
Dual infeasibility......:   2.3568935354300891e-09    2.3568935354300891e-09
Constraint violation....:   2.6905837557933410e-09    2.6905837557933410e-09
Variable bound violation:   5.8928328883212089e-09    5.8928328883212089e-09
Complementarity.........:   2.6603538266634660e-11    2.6603538266634660e-11
Overall NLP error.......:   2.6905837557933410e-09    2.6905837557933410e-09


Number of objective function evaluations             = 113
Number of objective gradient evaluations             = 112
Number of equality constraint evaluations            = 113
Number of inequality constraint evaluations          = 113
Number of equality constraint Jacobian evaluations   = 112
Number of inequality constraint Jacobian evaluations = 112
Number of Lagrangian Hessian evaluations             = 0
Total seconds in IPOPT                               = 84.318

EXIT: Optimal Solution Found.
WARNING (W1002): Setting Var 'cov_y[5,5]' to a numeric value
`1.0000000058928329` outside the bounds (0, 1).
    See also https://pyomo.readthedocs.io/en/latest/errors.html#w1002
[[ 1.20146398e+04  3.68501293e+02 -3.66901005e+03  2.55377403e+04
  -8.00907363e+03]
 [ 3.68501293e+02  2.88876229e+01  3.36919460e+01  8.86552040e+02
  -4.13155677e+02]
 [-3.66901005e+03  3.36919460e+01  3.92836117e+03 -9.08820664e+03
  -5.93884345e+01]
 [ 2.55377403e+04  8.86552040e+02 -9.08820664e+03  7.01896208e+04
  -1.78199162e+04]
 [-8.00907363e+03 -4.13155677e+02 -5.93884345e+01 -1.78199162e+04
   1.12467012e+04]]
trace: 97408.21054668962
det: 1.4166876939912604e+16
[8.60706546e+04 7.65928076e+03 2.65613515e+03 1.01416241e+03
 7.97762005e+00]
Ads.gas_inlet.F :  0.0
Ads.gas_outlet.F :  0.0
Ads.gas_outlet.T :  0.0
Des.gas_inlet.F :  0.0
Des.gas_outlet.F :  0.0
Des.gas_outlet.T :  1.0
Ads.T_g.Value(19,10) :  1.0
Ads.T_g.Value(23,10) :  0.0
Ads.T_g.Value(28,10) :  0.0
Ads.gas_outlet.z("CO2").static :  0.7
Des.gas_outlet.z("CO2").static :  1.0
Ads.gas_outlet.z("CO2").dynamic
[0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.25 0.25 0.25 0.25 0.   0.25 0.25 0.25 0.25 0.
 0.21 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  ]
Des.gas_outlet.z("CO2").dynamic
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
Ads.z("CO2",19,10)
[0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.04 0.04 0.03 0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.04 0.04 0.04 0.04 0.04 0.04 0.04
 0.04 0.04 0.   0.   0.   0.04 0.04 0.   0.   0.   0.   0.  ]
Ads.z("CO2",23,10)
[0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.21 0.21 0.21 0.21 0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.17 0.21 0.21 0.   0.21 0.17 0.21 0.21 0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.11 0.21 0.21 0.17 0.1  0.11
 0.21 0.21 0.21 0.14 0.15 0.21 0.21 0.21 0.14 0.15 0.   0.  ]
Ads.z("CO2",28,10)
[0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.25 0.27
 0.27 0.21 0.   0.   0.02 0.03 0.   0.   0.25 0.27 0.27 0.21 0.   0.25
 0.27 0.27 0.21 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.14 0.27
 0.27 0.27 0.05 0.14 0.27 0.27 0.27 0.   0.   0.   0.   0.   0.   0.
 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  ]
pyomo calculated cost: 15000.000099931394

Test C: run a test with MINLP, D-optimality, and MILP_A initialization
Static-cost measurement idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Dynamic-cost measurement idx: [11, 12, 13, 14, 15]
Minimal time interval between two samples: 10.0
Maximum number of samples for each measurement: 5
Maximum number of samples for all measurements: 20
cost list of all measurements, including SCMs and time points for DCMs: [1000, 1000, 500, 1000, 1000, 500, 1000, 1000, 1000, 7000, 7000, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
DCMs installation costs: [100, 100, 500, 500, 500]
Static-cost measurement idx:  0
Static-cost measurement idx:  1
Static-cost measurement idx:  2
Static-cost measurement idx:  3
Static-cost measurement idx:  4
Static-cost measurement idx:  5
Static-cost measurement idx:  6
Static-cost measurement idx:  7
Static-cost measurement idx:  8
Static-cost measurement idx:  9
Static-cost measurement idx:  10
Dynamic-cost measurement idx:  11
Dynamic-cost measurement idx:  12
Dynamic-cost measurement idx:  13
Dynamic-cost measurement idx:  14
Dynamic-cost measurement idx:  15
Number of binary decisions: 561
Dimension after dynamic flatten: [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, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560]
Dimension after flatten: []
Error covariance matrix option: CovarianceStructure.measure_correlation
Error matrix: [[1.   0.   0.   ... 0.   0.   0.  ]
 [0.   1.   0.   ... 0.   0.   0.  ]
 [0.   0.   1.   ... 0.   0.   0.  ]
 ...
 [0.   0.   0.   ... 0.01 0.   0.  ]
 [0.   0.   0.   ... 0.   0.01 0.  ]
 [0.   0.   0.   ... 0.   0.   0.01]]
Number of unit FIMs: 314721
Grey-box initialize inputs with an identity matrix.
using solution at 5000.0  to initialize, file name: ./rotary_results/MILP_5000_a
Initialization choice: 0 0
Initialization choice: 0 2
Initialization choice: 0 3
Initialization choice: 0 4
Initialization choice: 0 5
Initialization choice: 0 6
Initialization choice: 2 0
Initialization choice: 2 2
Initialization choice: 2 3
Initialization choice: 2 4
Initialization choice: 2 5
Initialization choice: 2 6
Initialization choice: 3 0
Initialization choice: 3 2
Initialization choice: 3 3
Initialization choice: 3 4
Initialization choice: 3 5
Initialization choice: 3 6
Initialization choice: 4 0
Initialization choice: 4 2
Initialization choice: 4 3
Initialization choice: 4 4
Initialization choice: 4 5
Initialization choice: 4 6
Initialization choice: 5 0
Initialization choice: 5 2
Initialization choice: 5 3
Initialization choice: 5 4
Initialization choice: 5 5
Initialization choice: 5 6
Initialization choice: 6 0
Initialization choice: 6 2
Initialization choice: 6 3
Initialization choice: 6 4
Initialization choice: 6 5
Initialization choice: 6 6
if_install_dynamic is initialized to be: [0, 0, 0, 0, 0]
Initialize with solution: [[1. 0. 1. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [1. 0. 1. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]]
warmstart FIM computed by integer decisions: [[ 5.40760686e+03 -8.64454984e+00 -4.17732111e+03  1.07727150e+04
  -3.13278464e+02]
 [-8.64454984e+00  4.65898315e+00  7.61917642e+00  3.63191106e+01
  -3.59191986e+00]
 [-4.17732111e+03  7.61917642e+00  3.29383764e+03 -8.51150752e+03
   2.14217373e+02]
 [ 1.07727150e+04  3.63191106e+01 -8.51150752e+03  2.46045473e+04
  -6.28365021e+02]
 [-3.13278464e+02 -3.59191986e+00  2.14217373e+02 -6.28365021e+02
   1.06532735e+02]]
Warmstart initialize FIM with:  [[ 5.40760696e+03 -8.64454984e+00 -4.17732111e+03  1.07727150e+04
  -3.13278464e+02]
 [-8.64454984e+00  4.65908315e+00  7.61917642e+00  3.63191106e+01
  -3.59191986e+00]
 [-4.17732111e+03  7.61917642e+00  3.29383774e+03 -8.51150752e+03
   2.14217373e+02]
 [ 1.07727150e+04  3.63191106e+01 -8.51150752e+03  2.46045474e+04
  -6.28365021e+02]
 [-3.13278464e+02 -3.59191986e+00  2.14217373e+02 -6.28365021e+02
   1.06532835e+02]]
Warmstart logdet: 26.186017130892324
Warmstart eigen value: [3.25249444e+04 7.60914544e+02 9.93852680e+01 2.85870641e+01
 3.35273112e+00]
warmstart initialize total measure: 6.0
warmstart initialize total dynamic:  0.0
warmstart initialize cost: 5000.0
Starting MindtPy version 1.0.0 using OA algorithm
iteration_limit: 150
stalling_limit: 1000
time_limit: 600
strategy: OA
add_regularization: None
call_after_main_solve: <pyomo.contrib.gdpopt.util._DoNothing object at 0x127cc58a0>
call_before_subproblem_solve: <bound method MeasurementOptimizer.customized_warmstart of <measure_optimize.MeasurementOptimizer object at 0x365ecb1f0>>
call_after_subproblem_solve: <pyomo.contrib.gdpopt.util._DoNothing object at 0x127cc5900>
call_after_subproblem_feasible: <pyomo.contrib.gdpopt.util._DoNothing object at 0x127cc5930>
tee: true
logger: <Logger pyomo.contrib.mindtpy (INFO)>
logging_level: 20
integer_to_binary: false
add_no_good_cuts: false
use_tabu_list: false
single_tree: false
solution_pool: false
num_solution_iteration: 5
cycling_check: true
feasibility_norm: L_infinity
differentiate_mode: reverse_symbolic
use_mcpp: false
calculate_dual_at_solution: true
use_fbbt: false
use_dual_bound: true
partition_obj_nonlinear_terms: true
quadratic_strategy: 0
move_objective: false
add_cuts_at_incumbent: false
heuristic_nonconvex: false
init_strategy: rNLP
level_coef: 0.5
solution_limit: 10
sqp_lag_scaling_coef: fixed
fp_cutoffdecr: 0.1
fp_iteration_limit: 20
fp_projcuts: true
fp_transfercuts: true
fp_projzerotol: 0.0001
fp_mipgap: 0.01
fp_discrete_only: true
fp_main_norm: L1
fp_norm_constraint: true
fp_norm_constraint_coef: 1.0
add_slack: false
max_slack: 1000.0
OA_penalty_factor: 1000.0
equality_relaxation: false
linearize_inactive: false
nlp_solver: cyipopt
nlp_solver_args:
  options: {bound_push: 1.0e-10, hessian_approximation: limited-memory, linear_solver: ma57,
  max_iter: 3000, output_file: console_output, warm_start_bound_frac: 1.0e-10, warm_start_bound_push: 1.0e-10,
  warm_start_init_point: 'yes', warm_start_mult_bound_push: 1.0e-10, warm_start_slack_bound_frac: 1.0e-10,
  warm_start_slack_bound_push: 1.0e-10}
mip_solver: gurobi
mip_solver_args:
  options: {NumericFocus: '3'}
mip_solver_mipgap: 0.0001
threads: 0
regularization_mip_threads: 0
solver_tee: false
mip_solver_tee: true
nlp_solver_tee: true
mip_regularization_solver: None
absolute_bound_tolerance: 0.0001
relative_bound_tolerance: 0.001
small_dual_tolerance: 1.0e-08
integer_tolerance: 1.0e-05
constraint_tolerance: 1.0e-06
variable_tolerance: 1.0e-08
zero_tolerance: 1.0e-08
obj_bound: 1000000000000000.0
continuous_var_bound: 10000000000.0
integer_var_bound: 1000000000.0
initial_bound_coef: 0.1

-----------------------------------------------------------------------------------------------
               Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo (MindtPy)                
-----------------------------------------------------------------------------------------------
For more information, please visit 
https://pyomo.readthedocs.io/en/stable/contributed_packages/mindtpy.html
If you use this software, please cite the following:
Bernal, David E., et al. Mixed-integer nonlinear decomposition toolbox for Pyomo (MindtPy).
Computer Aided Chemical Engineering. Vol. 44. Elsevier, 2018. 895-900.

zsh: segmentation fault  python rotary_bed_MO.py

This is for the D-optimality relaxed test problems for the rotary bed.

[adowling2]

Here is the full output:
seg_fault.txt

[adowling2]

@jialuw96 Did you need to run pyomo build-extensions to overcome an ASL error on the CRC? Here is what I got when I tried to run pyomo build-extensions:

**** Building AMPL External function demo library ****
ERROR: OSError: cmake not found in the system PATH


**** Building APPSI ****
ERROR: ModuleNotFoundError: No module named 'pybind11'

**** Building MCPP library ****
ERROR: RuntimeError: Cannot identify the location of the MCPP source
distribution

**** Building PyNumero libraries ****
ERROR: OSError: cmake not found in the system PATH
INFO: Finished building Pyomo extensions.
INFO: The following extensions were built:
    [FAIL]  ampl_function_demo
    [FAIL]  appsi
    [FAIL]  mcpp
    [FAIL]  pynumero

[jialuw96]

@adowling2 I didn't need to run pyomo build-extensions on my environments on CRC; What is that ASL error you had?

[adowling2]

I think installing idaes fixed it.

[adowling2]

@jialuw96 Do you recall how you fixed this error on the CRC?

Exception of type: DYNAMIC_LIBRARY_FAILURE in file "../../src/Common/IpLibraryLoader.cpp" at line 67:
 Exception message: libhsl.so: cannot open shared object file: No such file or directory

EXIT: Library loading failure.

module load ipopt/hsl did not work.

[jialuw96]

@adowling2 I didn't have this error before; I was using module load ipopt/hsl/3.12.8 and it worked.

[adowling2]

@jialuw96 That worked! I am running the code in the alex-testing branch. Here is what I get when I try to rerun the rotary bed paper results:

====Solving with budget: 25000.0 ====
using solution at 25000.0  to initialize, file name: ./rotary_results/LP_25000_a
Initialization choice: 0 0
Initialization choice: 0 1
Initialization choice: 0 2
Initialization choice: 0 3
Initialization choice: 0 4
Initialization choice: 0 5
Initialization choice: 0 6
Initialization choice: 0 7
Initialization choice: 0 8
Initialization choice: 0 9
Initialization choice: 0 10
Initialization choice: 0 268
Initialization choice: 0 300
Initialization choice: 0 305
Initialization choice: 0 320
Initialization choice: 0 325
Initialization choice: 0 420
Initialization choice: 0 425
Initialization choice: 0 440
Initialization choice: 0 445
Initialization choice: 0 450
Initialization choice: 0 493
Initialization choice: 0 498
Initialization choice: 0 503
Initialization choice: 0 508
Initialization choice: 0 513
Initialization choice: 1 0
Initialization choice: 1 1
Initialization choice: 1 2
Initialization choice: 1 3
Initialization choice: 1 4
Initialization choice: 1 5
Initialization choice: 1 6
Initialization choice: 1 7
Initialization choice: 1 8
Initialization choice: 1 9
Initialization choice: 1 10
Initialization choice: 1 268
Initialization choice: 1 300
Initialization choice: 1 305
Initialization choice: 1 320
Initialization choice: 1 325
Initialization choice: 1 420
Initialization choice: 1 425
Initialization choice: 1 440
Initialization choice: 1 445
Initialization choice: 1 450
Initialization choice: 1 493
Initialization choice: 1 498
Initialization choice: 1 503
Initialization choice: 1 508
Initialization choice: 1 513
Initialization choice: 2 0
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if_install_dynamic is initialized to be: [0, 0, 1.0, 1.0, 1.0]
Initialize with solution: [[1. 1. 1. ... 0. 0. 0.]
 [1. 1. 1. ... 0. 0. 0.]
 [1. 1. 1. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]]
warmstart FIM computed by integer decisions: [[ 1.35757604e+04  4.57138341e+02 -3.79877328e+03  2.92446661e+04
  -1.02653600e+04]
 [ 4.57138341e+02  3.61653951e+01  2.71715689e+01  1.13321944e+03
  -5.48953828e+02]
 [-3.79877328e+03  2.71715689e+01  3.98419721e+03 -9.46221425e+03
   1.37524906e+02]
 [ 2.92446661e+04  1.13321944e+03 -9.46221425e+03  8.12454780e+04
  -2.35173081e+04]
 [-1.02653600e+04 -5.48953828e+02  1.37524906e+02 -2.35173081e+04
   1.46321680e+04]]
Warmstart initialize FIM with:  [[ 1.35757605e+04  4.57138341e+02 -3.79877328e+03  2.92446661e+04
  -1.02653600e+04]
 [ 4.57138341e+02  3.61654951e+01  2.71715689e+01  1.13321944e+03
  -5.48953828e+02]
 [-3.79877328e+03  2.71715689e+01  3.98419731e+03 -9.46221425e+03
   1.37524906e+02]
 [ 2.92446661e+04  1.13321944e+03 -9.46221425e+03  8.12454781e+04
  -2.35173081e+04]
 [-1.02653600e+04 -5.48953828e+02  1.37524906e+02 -2.35173081e+04
   1.46321681e+04]]
Warmstart logdet: 37.825791541581474
Warmstart eigen value: [1.00848574e+05 8.58968936e+03 2.94125811e+03 1.08456354e+03
 9.68494560e+00]
warmstart initialize total measure: 26.0
warmstart initialize total dynamic:  15.0
warmstart initialize cost: 25000.0
Gurobi Optimizer version 10.0.3 build v10.0.3rc0 (linux64)

CPU model: Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz, instruction set [SSE2|AVX|AVX2]
Thread count: 24 physical cores, 24 logical processors, using up to 24 threads

Optimize a model with 471933 rows, 157664 columns and 1114562 nonzeros
Model fingerprint: 0x4bf4f86b
Variable types: 18 continuous, 157646 integer (157646 binary)
Coefficient statistics:
  Matrix range     [9e-13, 3e+04]
  Objective range  [1e+00, 1e+00]
  Bounds range     [1e+00, 1e+00]
  RHS range        [1e-04, 2e+04]
Warning: Model contains large matrix coefficient range
         Consider reformulating model or setting NumericFocus parameter
         to avoid numerical issues.
Presolve removed 5901 rows and 18 columns (presolve time = 5s) ...
Presolve removed 5901 rows and 18 columns (presolve time = 10s) ...
Presolve removed 5901 rows and 18 columns (presolve time = 15s) ...
Presolve removed 471344 rows and 157122 columns
Presolve time: 18.57s
Presolved: 589 rows, 542 columns, 5913 nonzeros
Variable types: 0 continuous, 542 integer (536 binary)
Found heuristic solution: objective 38630.627206

Root simplex log...

Iteration    Objective       Primal Inf.    Dual Inf.      Time
       0    1.3380622e+05   2.860000e+02   0.000000e+00     19s
     319    1.1347377e+05   0.000000e+00   0.000000e+00     19s

Root relaxation: objective 1.134738e+05, 319 iterations, 0.01 seconds (0.00 work units)

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

*    0     0               0    113473.76954 113473.770  0.00%     -   18s

Explored 1 nodes (319 simplex iterations) in 18.96 seconds (7.44 work units)
Thread count was 24 (of 24 available processors)

Solution count 2: 113474 38630.6 

Optimal solution found (tolerance 1.00e-04)
Best objective 1.134737695435e+05, best bound 1.134737695435e+05, gap 0.0000%
[[ 1.35757605e+04  4.57138341e+02 -3.79877328e+03  2.92446661e+04
  -1.02653600e+04]
 [ 4.57138341e+02  3.61654951e+01  2.71715689e+01  1.13321944e+03
  -5.48953828e+02]
 [-3.79877328e+03  2.71715689e+01  3.98419731e+03 -9.46221425e+03
   1.37524906e+02]
 [ 2.92446661e+04  1.13321944e+03 -9.46221425e+03  8.12454781e+04
  -2.35173081e+04]
 [-1.02653600e+04 -5.48953828e+02  1.37524906e+02 -2.35173081e+04
   1.46321681e+04]]
trace: 113473.76954346175
det: 2.676286110229166e+16
[1.00848574e+05 8.58968936e+03 2.94125811e+03 1.08456354e+03
 9.68494560e+00]
Ads.gas_inlet.F :  1.0
Ads.gas_outlet.F :  1.0
Ads.gas_outlet.T :  1.0
Des.gas_inlet.F :  1.0
Des.gas_outlet.F :  1.0
Des.gas_outlet.T :  1.0
Ads.T_g.Value(19,10) :  1.0
Ads.T_g.Value(23,10) :  1.0
Ads.T_g.Value(28,10) :  1.0
Ads.gas_outlet.z("CO2").static :  1.0
Des.gas_outlet.z("CO2").static :  1.0
Ads.gas_outlet.z("CO2").dynamic
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
Des.gas_outlet.z("CO2").dynamic
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
Ads.z("CO2",19,10)
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.
 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
Ads.z("CO2",23,10)
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1.]
Ads.z("CO2",28,10)
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1.
 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
pyomo calculated cost: 25000.0
====Solving with budget: 26000.0 ====
using solution at 26000.0  to initialize, file name: ./rotary_results/LP_26000_a
Traceback (most recent call last):
  File "/afs/crc.nd.edu/user/a/adowling/DowlingLab/measurement-opt/rotary_bed_MO.py", line 370, in <module>
    rotary_experiment(mip_option=True, 
  File "/afs/crc.nd.edu/user/a/adowling/DowlingLab/measurement-opt/rotary_bed_MO.py", line 316, in rotary_experiment
    calculator.update_budget(b)
  File "/afs/crc.nd.edu/user/a/adowling/DowlingLab/measurement-opt/measure_optimize.py", line 1941, in update_budget
    self._initialize_binary(initial_file_name, round_solution=round_solution_opt)
  File "/afs/crc.nd.edu/user/a/adowling/DowlingLab/measurement-opt/measure_optimize.py", line 2036, in _initialize_binary
    with open(y_init_file, "rb") as f:
FileNotFoundError: [Errno 2] No such file or directory: './rotary_results/LP_26000_a'

Is this a missing file?

[adowling2]

Here is the specific commit I ran if you want to see the code: 717986a

[jialuw96]

@adowling2 I checked it and this is because a typo in the ranges we defined; For the paper results, we ran the results from budget 1k to 25k, so budget_ranges=np.linspace(1000, 25000,25), yet in the code in alex-testing branch we wrote it as budget_ranges=np.linspace(1000, 26000,26). That is why the code cannot find a missing file; because we never solved 26000 as a budget so we do not have the initialization file for it.

To rerun the paper results, it should be an easy fix if you change budget_ranges in line 240 and 247 in rotary_bed_MO.py to np.linspace(1000, 25000,25) so the final solve is 25000. I can do the fix if needed. Thank you!