OptimalControl.jl
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control-toolbox
Improve performance
@PierreMartinon @BaptisteCbl your mission if you accept it is to improve drastically the performance of our solver, thanks to wise profiling and benchmarking.
For now we have this kind of results: Results are on classic goddard, with a grid of size 30. JUMP resolution
Solving...
This is Ipopt version 3.14.13, running with linear solver MUMPS 5.6.0.
Number of nonzeros in equality constraint Jacobian...: 573
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 1710
Total number of variables............................: 125
variables with only lower bounds: 32
variables with lower and upper bounds: 93
variables with only upper bounds: 0
Total number of equality constraints.................: 93
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0100000e+00 3.96e-01 1.42e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.0099579e+00 3.82e-01 1.55e+01 -1.7 4.61e-01 - 1.04e-02 3.59e-02f 1
2 1.0123693e+00 9.05e-03 3.60e+03 -1.7 4.80e-01 - 5.61e-02 9.90e-01f 1
3 1.0089467e+00 6.48e-03 2.52e+03 -1.7 1.30e-01 - 6.51e-01 2.87e-01h 1
4 1.0012838e+00 2.56e-03 2.85e+02 -1.7 2.80e-01 - 9.15e-01 9.90e-01h 1
5 1.0055718e+00 6.94e-04 7.38e+04 -1.7 2.99e-01 - 6.99e-01 9.90e-01f 1
6 1.0072751e+00 2.21e-03 1.25e+06 -1.7 2.56e-01 - 2.70e-01 1.00e+00f 1
7 1.0072253e+00 1.23e-03 2.25e+05 -1.7 2.87e-01 - 8.86e-01 1.00e+00h 1
8 1.0067740e+00 2.09e-04 6.58e-01 -1.7 1.00e-01 - 1.00e+00 1.00e+00h 1
9 1.0067862e+00 1.61e-07 9.53e+02 -3.8 3.16e-03 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.0087743e+00 5.46e-04 1.44e-01 -3.8 1.36e-01 - 1.00e+00 1.00e+00h 1
11 1.0086566e+00 4.36e-05 4.27e-04 -3.8 9.79e-02 - 1.00e+00 1.00e+00h 1
12 1.0102082e+00 2.49e-04 2.57e+03 -5.7 1.12e-01 - 8.27e-01 1.00e+00h 1
13 1.0119095e+00 4.94e-04 3.83e+02 -5.7 1.51e-01 - 8.51e-01 7.35e-01h 1
14 1.0122542e+00 2.19e-04 2.17e+01 -5.7 1.81e-01 - 9.43e-01 6.00e-01h 1
15 1.0124547e+00 1.52e-04 1.27e-03 -5.7 2.00e-01 - 1.00e+00 1.00e+00f 1
16 1.0124458e+00 5.21e-06 9.77e-05 -5.7 7.70e-02 - 1.00e+00 1.00e+00h 1
17 1.0124460e+00 1.31e-07 7.94e-07 -5.7 1.13e-02 - 1.00e+00 1.00e+00h 1
18 1.0125075e+00 1.19e-05 1.17e+01 -8.6 1.00e-01 - 8.33e-01 7.69e-01h 1
19 1.0125219e+00 1.25e-05 6.03e+00 -8.6 1.67e-01 - 8.53e-01 7.51e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.0125259e+00 6.81e-06 1.38e+00 -8.6 9.08e-02 - 1.00e+00 8.70e-01h 1
21 1.0125263e+00 3.46e-07 7.65e-06 -8.6 4.05e-02 - 1.00e+00 1.00e+00f 1
22 1.0125262e+00 3.43e-08 6.14e-07 -8.6 1.33e-02 - 1.00e+00 1.00e+00h 1
23 1.0125262e+00 2.18e-09 4.54e-08 -8.6 3.39e-03 - 1.00e+00 1.00e+00h 1
24 1.0125262e+00 1.87e-11 4.19e-10 -8.6 3.07e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 24
(scaled) (unscaled)
Objective...............: -1.0125262367726526e+00 1.0125262367726526e+00
Dual infeasibility......: 4.1912912891737240e-10 4.1912912891737240e-10
Constraint violation....: 1.8681001190401503e-11 1.8681001190401503e-11
Variable bound violation: 1.4092401593955658e-37 1.4092401593955658e-37
Complementarity.........: 2.5080751303192126e-09 2.5080751303192126e-09
Overall NLP error.......: 2.5080751303192126e-09 2.5080751303192126e-09
Number of objective function evaluations = 25
Number of objective gradient evaluations = 25
Number of equality constraint evaluations = 25
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 25
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 24
Total seconds in IPOPT = 0.057
EXIT: Optimal Solution Found.
Local solution found
Objective value = 1.0125262367726526
JUMP -> MathOptNLPModel NLPModelsIpopt resolution
This is Ipopt version 3.14.13, running with linear solver MUMPS 5.6.0.
Number of nonzeros in equality constraint Jacobian...: 573
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 1710
Total number of variables............................: 125
variables with only lower bounds: 32
variables with lower and upper bounds: 93
variables with only upper bounds: 0
Total number of equality constraints.................: 93
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0100000e+00 3.96e-01 1.42e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.0130396e+00 9.59e-03 1.15e+04 -1.4 4.74e-01 - 1.09e-02 9.90e-01f 1
2 1.0122507e+00 9.02e-03 1.08e+04 -1.3 5.04e-02 2.0 1.00e+00 5.95e-02h 1
3 1.0072459e+00 5.60e-03 6.41e+03 -1.7 7.41e-02 1.5 1.00e+00 3.98e-01h 1
4 1.0067482e+00 5.08e-03 1.22e+04 -1.1 1.30e+00 - 6.31e-01 9.28e-02h 1
5 1.0025563e+00 1.69e-03 1.73e+03 -1.9 1.63e-01 - 8.09e-01 8.49e-01h 1
6 1.0027663e+00 8.58e-05 2.26e+02 -7.7 4.12e-02 - 3.64e-01 9.79e-01h 1
7 1.0027961e+00 9.25e-07 7.40e+01 -3.8 5.79e-03 - 9.10e-01 9.92e-01h 1
8 1.0028949e+00 5.73e-07 7.99e+02 -5.0 9.87e-03 - 9.53e-01 1.00e+00h 1
9 1.0071683e+00 7.52e-04 1.02e+03 -4.6 2.65e-01 - 4.03e-01 8.63e-01f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.0099600e+00 1.07e-03 9.53e+02 -4.6 3.54e-01 - 2.49e-01 5.26e-01h 1
11 1.0107794e+00 1.17e-03 8.81e+02 -4.2 1.08e+00 - 3.84e-01 4.66e-01h 1
12 1.0095781e+00 1.33e-03 9.59e+03 -3.8 2.14e+00 - 1.00e+00 2.49e-01h 1
13 1.0104206e+00 1.31e-04 5.81e-03 -4.5 1.07e-01 - 1.00e+00 1.00e+00h 1
14 1.0118314e+00 4.06e-04 3.81e+02 -5.0 1.83e-01 - 1.00e+00 8.03e-01h 1
15 1.0122730e+00 1.40e-04 2.32e-03 -5.2 1.17e-01 - 1.00e+00 1.00e+00h 1
16 1.0124602e+00 1.01e-04 1.59e-03 -5.8 1.45e-01 - 1.00e+00 1.00e+00h 1
17 1.0125015e+00 2.16e-05 3.68e-04 -6.2 1.10e-01 - 1.00e+00 1.00e+00h 1
18 1.0125225e+00 1.79e-05 2.49e-04 -7.0 1.56e-01 - 1.00e+00 1.00e+00h 1
19 1.0125258e+00 6.06e-06 6.12e-03 -7.8 8.63e-02 - 1.00e+00 9.99e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.0125263e+00 3.13e-07 6.75e-06 -8.6 3.89e-02 - 1.00e+00 1.00e+00h 1
21 1.0125263e+00 6.22e-08 1.07e-06 -9.6 1.74e-02 - 1.00e+00 1.00e+00h 1
22 1.0125263e+00 7.47e-09 1.64e-07 -11.2 6.34e-03 - 1.00e+00 1.00e+00h 1
23 1.0125263e+00 3.67e-10 8.95e-09 -11.8 1.36e-03 - 1.00e+00 1.00e+00h 1
24 1.0125263e+00 1.32e-12 1.79e-10 -12.1 8.11e-05 - 1.00e+00 1.00e+00h 1
25 1.0125263e+00 3.05e-16 1.04e-11 -12.3 1.30e-06 - 1.00e+00 1.00e+00h 1
26 1.0125263e+00 2.22e-16 1.44e-11 -12.3 6.90e-09 - 1.00e+00 1.00e+00h 1
27 1.0125263e+00 2.22e-16 1.35e-13 -12.3 4.61e-10 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 27
(scaled) (unscaled)
Objective...............: -1.0125263397980653e+00 1.0125263397980653e+00
Dual infeasibility......: 1.3533594327169007e-13 1.3533594327169007e-13
Constraint violation....: 2.2204460492503131e-16 2.2204460492503131e-16
Variable bound violation: 9.9162263955676622e-09 9.9162263955676622e-09
Complementarity.........: 5.0227126311592196e-13 5.0227126311592196e-13
Overall NLP error.......: 5.0227126311592196e-13 5.0227126311592196e-13
Number of objective function evaluations = 28
Number of objective gradient evaluations = 28
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 28
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 27
Total seconds in IPOPT = 0.068
EXIT: Optimal Solution Found.
CTDirect OCPModel -> ADNLPModel (v0.5) NLPModelsIpopt resolution
This is Ipopt version 3.14.13, running with linear solver MUMPS 5.6.0.
Number of nonzeros in equality constraint Jacobian...: 574
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 7875
Total number of variables............................: 125
variables with only lower bounds: 31
variables with lower and upper bounds: 62
variables with only upper bounds: 0
Total number of equality constraints.................: 94
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 -1.0100000e+00 9.00e-01 2.00e+00 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 -1.0030260e+00 7.00e-01 2.48e+02 -0.7 9.00e-01 - 4.85e-03 2.22e-01f 1
2 -1.0093386e+00 1.84e-01 1.18e+02 -0.7 7.00e-01 2.0 9.97e-01 7.37e-01h 1
3 -1.0096326e+00 1.07e-01 1.22e+02 -0.4 3.59e-01 1.5 1.00e+00 4.17e-01h 1
4 -1.0084386e+00 1.07e-03 2.97e+01 -0.6 3.26e-01 - 8.12e-01 9.90e-01h 1
5 -1.0080173e+00 8.40e-05 1.77e+01 -2.2 5.92e-02 - 9.57e-01 9.90e-01h 1
6 -1.0080420e+00 1.73e-07 2.79e+00 -7.7 2.19e-03 - 9.85e-01 9.98e-01h 1
7 -1.0097714e+00 1.37e-04 3.07e+02 -4.7 7.07e-02 - 7.89e-01 1.00e+00f 1
8 -1.0124338e+00 9.26e-04 6.15e+00 -4.7 2.00e-01 - 1.00e+00 9.84e-01h 1
9 -1.0123613e+00 1.24e-04 4.87e-03 -5.3 1.13e-01 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 -1.0124870e+00 7.79e-05 4.60e+00 -5.9 1.25e-01 - 1.00e+00 9.86e-01h 1
11 -1.0125120e+00 1.97e-05 5.85e-01 -6.3 1.20e-01 - 1.00e+00 9.93e-01h 1
12 -1.0125239e+00 1.49e-05 2.24e-01 -7.1 1.52e-01 - 1.00e+00 9.94e-01h 1
13 -1.0125262e+00 5.03e-06 1.80e-05 -8.1 8.24e-02 - 1.00e+00 1.00e+00h 1
14 -1.0125262e+00 3.29e-07 8.64e-05 -8.4 3.54e-02 - 1.00e+00 1.00e+00h 1
15 -1.0125263e+00 5.03e-08 5.30e-07 -10.0 1.66e-02 - 1.00e+00 1.00e+00h 1
16 -1.0125263e+00 6.43e-09 8.25e-08 -11.0 5.87e-03 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 16
(scaled) (unscaled)
Objective...............: -1.0125263394411863e+00 -1.0125263394411863e+00
Dual infeasibility......: 8.2471633469491931e-08 8.2471633469491931e-08
Constraint violation....: 1.8099726162290608e-09 6.4261801435705479e-09
Variable bound violation: 8.3128719602365564e-09 8.3128719602365564e-09
Complementarity.........: 6.6254607323767334e-10 6.6254607323767334e-10
Overall NLP error.......: 1.8099726162290608e-09 8.2471633469491931e-08
Number of objective function evaluations = 17
Number of objective gradient evaluations = 17
Number of equality constraint evaluations = 17
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 17
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 16
Total seconds in IPOPT = 3.009
EXIT: Optimal Solution Found.
CTDirect OCPModel -> ADNLPModel (v0.6) NLPModelsIpopt resolution
This is Ipopt version 3.14.13, running with linear solver MUMPS 5.6.0.
Number of nonzeros in equality constraint Jacobian...: 574
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 341
Total number of variables............................: 125
variables with only lower bounds: 31
variables with lower and upper bounds: 62
variables with only upper bounds: 0
Total number of equality constraints.................: 94
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 -1.0100000e+00 9.00e-01 2.00e+00 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 -1.0030260e+00 7.00e-01 2.48e+02 -0.7 9.00e-01 - 4.85e-03 2.22e-01f 1
2 -1.0093386e+00 1.84e-01 1.18e+02 -0.7 7.00e-01 2.0 9.97e-01 7.37e-01h 1
3 -1.0096326e+00 1.07e-01 1.22e+02 -0.4 3.59e-01 1.5 1.00e+00 4.17e-01h 1
4 -1.0084386e+00 1.07e-03 2.97e+01 -0.6 3.26e-01 - 8.12e-01 9.90e-01h 1
5 -1.0080173e+00 8.40e-05 1.77e+01 -2.2 5.92e-02 - 9.57e-01 9.90e-01h 1
6 -1.0080420e+00 1.73e-07 2.79e+00 -7.7 2.19e-03 - 9.85e-01 9.98e-01h 1
7 -1.0097714e+00 1.37e-04 3.07e+02 -4.7 7.07e-02 - 7.89e-01 1.00e+00f 1
8 -1.0124338e+00 9.26e-04 6.15e+00 -4.7 2.00e-01 - 1.00e+00 9.84e-01h 1
9 -1.0123613e+00 1.24e-04 4.87e-03 -5.3 1.13e-01 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 -1.0124870e+00 7.79e-05 4.60e+00 -5.9 1.25e-01 - 1.00e+00 9.86e-01h 1
11 -1.0125120e+00 1.97e-05 5.85e-01 -6.3 1.20e-01 - 1.00e+00 9.93e-01h 1
12 -1.0125239e+00 1.49e-05 2.24e-01 -7.1 1.52e-01 - 1.00e+00 9.94e-01h 1
13 -1.0125262e+00 5.03e-06 1.80e-05 -8.1 8.24e-02 - 1.00e+00 1.00e+00h 1
14 -1.0125262e+00 3.28e-07 1.04e-06 -8.4 3.53e-02 - 1.00e+00 1.00e+00h 1
15 -1.0125263e+00 5.01e-08 5.29e-07 -9.9 1.66e-02 - 1.00e+00 1.00e+00h 1
16 -1.0125263e+00 6.48e-09 8.31e-08 -11.7 5.91e-03 - 1.00e+00 1.00e+00h 1
17 -1.0125263e+00 2.83e-10 3.33e-09 -12.3 1.20e-03 - 1.00e+00 1.00e+00h 1
18 -1.0125263e+00 7.57e-13 8.43e-12 -12.3 6.11e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 18
(scaled) (unscaled)
Objective...............: -1.0125263392620425e+00 -1.0125263392620425e+00
Dual infeasibility......: 8.4322266966417110e-12 8.4322266966417110e-12
Constraint violation....: 1.5765166949677223e-13 7.5696393597723964e-13
Variable bound violation: 9.9161631406108341e-09 9.9161631406108341e-09
Complementarity.........: 5.9181867333975404e-13 5.9181867333975404e-13
Overall NLP error.......: 5.9181867333975404e-13 8.4322266966417110e-12
Number of objective function evaluations = 19
Number of objective gradient evaluations = 19
Number of equality constraint evaluations = 19
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 19
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 18
Total seconds in IPOPT = 8.586
EXIT: Optimal Solution Found.
To have these results I used the code in : https://github.com/BaptisteCbl/JumpVSADNLP
A bench:
using BenchmarkTools
function test_alloc_good()
function get_view(Y, i, n_slice, N)
return @view Y[i:i+n_slice-1]
end
function set!(Z, i, n_slice, N, z)
Z[i:i+n_slice-1] = z
end
function core_for!(v, x, z, n)
(v[i] = 2x[i]+3z[i] for i ∈ 1:n)
end
n_slice = 50
n_disc = 2000
N = n_slice * n_disc
Y = rand(N)
Z = zeros(N)
x = zeros(n_slice)
v = zeros(n_slice)
for i ∈ 1:n_disc
#
z = get_view(Y, i, n_slice, N)
#
core_for!(v, x, z, n_slice)
set!(Z, i, n_slice, N, v)
#
x[:] = z
end
#
nothing
end
function test_alloc_bad()
function get(Y, i, n_slice, N)
return Y[i:i+n_slice-1]
end
function set!(Z, i, n_slice, N, z)
Z[i:i+n_slice-1] = z[:]
end
function core(x, z)
return 2x[:]+3z[:]
end
#function core_for(x, z, n)
# return [2x[i]+3z[i] for i ∈ 1:n]
#end
#function core_for!(v, x, z, n)
# v[:] = [2x[i]+3z[i] for i ∈ 1:n]
#end
n_slice = 50
n_disc = 2000
N = n_slice * n_disc
Y = rand(N)
Z = zeros(N)
x = zeros(n_slice)
v = zeros(n_slice)
for i ∈ 1:n_disc
#
z = get(Y, i, n_slice, N)
#
set!(Z, i, n_slice, N, core(x[:], z[:]))
#
x = z
end
#
nothing
end
println("Allocations and times for good and bad code")
println()
println("good code")
t_good = @benchmark test_alloc_good()
display(t_good)
println()
println("bad code")
t_bad = @benchmark test_alloc_bad()
display(t_bad)
println()
# print the ratio of the times
println("ratio of times: ", mean(t_bad.times)/mean(t_good.times))
println("ratio of allocations: ", mean(t_bad.memory)/mean(t_good.memory))
Results:
Allocations and times for good and bad code
good code
BenchmarkTools.Trial: 3534 samples with 1 evaluation.
Range (min … max): 332.106 μs … 84.731 ms ┊ GC (min … max): 0.00% … 73.71%
Time (median): 1.050 ms ┊ GC (median): 0.00%
Time (mean ± σ): 1.398 ms ± 2.073 ms ┊ GC (mean ± σ): 13.85% ± 13.54%
▇▂ ▅█▅▄▃▂▁▁ ▁▁ ▁
███▆████████▇▇█▇█████▆▆▇▅▅▅▆▄▅▅▁▄▅▃▅▄▅▄▇▆▆▅▃▆▇▃▅▃▄▅▅▅▅▅▆▅▅▃▅ █
332 μs Histogram: log(frequency) by time 8.57 ms <
Memory estimate: 1.53 MiB, allocs estimate: 6.
bad code
BenchmarkTools.Trial: 1699 samples with 1 evaluation.
Range (min … max): 1.623 ms … 81.262 ms ┊ GC (min … max): 0.00% … 0.00%
Time (median): 1.993 ms ┊ GC (median): 0.00%
Time (mean ± σ): 2.916 ms ± 3.547 ms ┊ GC (mean ± σ): 12.48% ± 17.07%
█▅▄▃▂▂▃▃▂▃▃▂▂▁ ▁
████████████████████▇▇▇▆▆▅▆▇▅▃▅▃▆▃▆▅▅▄▅▄▁▄▄▃▄▁▁▁▁▃▄▁▁▄▁▁▁▅ █
1.62 ms Histogram: log(frequency) by time 11.9 ms <
Memory estimate: 10.04 MiB, allocs estimate: 18006.
ratio of times: 2.0862225595372297
ratio of allocations: 6.5762081784386615
@jbcaillau @PierreMartinon @BaptisteCbl @joseph-gergaud
nlp_constraints from CTBase must be updated: https://github.com/control-toolbox/CTBase.jl/blob/bench/bench/bench_nlp_constraints.md
The code: https://github.com/control-toolbox/CTBase.jl/blob/bench/bench/bench_nlp_constraints.jl
@ocots @BaptisteCbl @amontoison well, take a look 🙂
- 10x speed up wrt to last year version
- 4x to 5x difference with pure
JuMPcode
At this price, more than acceptable decrease in performance... and no RK scheme to hard (hand) code.
NB. Tested with ma57 instead of MUMPS.
Method = (:direct, :adnlp, :ipopt)
This is Ipopt version 3.14.14, running with linear solver ma57.
Number of nonzeros in equality constraint Jacobian...: 634
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 341
Total number of variables............................: 125
variables with only lower bounds: 31
variables with lower and upper bounds: 62
variables with only upper bounds: 0
Total number of equality constraints.................: 94
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 -1.0100000e+00 9.00e-01 2.00e+00 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
Reallocating memory for MA57: lfact (8303)
1 -1.0030260e+00 7.00e-01 2.48e+02 -0.7 9.00e-01 - 4.85e-03 2.22e-01f 1
2 -1.0093386e+00 1.84e-01 1.18e+02 -0.7 7.00e-01 2.0 9.97e-01 7.37e-01h 1
3 -1.0096326e+00 1.07e-01 1.22e+02 -0.4 3.59e-01 1.5 1.00e+00 4.17e-01h 1
4 -1.0084386e+00 1.07e-03 2.97e+01 -0.6 3.26e-01 - 8.12e-01 9.90e-01h 1
5 -1.0080173e+00 8.40e-05 1.77e+01 -2.2 5.92e-02 - 9.57e-01 9.90e-01h 1
6 -1.0080420e+00 1.73e-07 2.79e+00 -7.7 2.19e-03 - 9.85e-01 9.98e-01h 1
7 -1.0097714e+00 1.37e-04 3.07e+02 -4.7 7.07e-02 - 7.89e-01 1.00e+00f 1
8 -1.0124338e+00 9.26e-04 6.15e+00 -4.7 2.00e-01 - 1.00e+00 9.84e-01h 1
9 -1.0123613e+00 1.24e-04 4.87e-03 -5.3 1.13e-01 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 -1.0124870e+00 7.79e-05 4.60e+00 -5.9 1.25e-01 - 1.00e+00 9.86e-01h 1
11 -1.0125120e+00 1.97e-05 5.85e-01 -6.3 1.20e-01 - 1.00e+00 9.93e-01h 1
12 -1.0125239e+00 1.49e-05 2.24e-01 -7.1 1.52e-01 - 1.00e+00 9.94e-01h 1
13 -1.0125262e+00 5.03e-06 1.80e-05 -8.1 8.24e-02 - 1.00e+00 1.00e+00h 1
14 -1.0125262e+00 3.29e-07 8.64e-05 -8.4 3.54e-02 - 1.00e+00 1.00e+00h 1
15 -1.0125263e+00 5.03e-08 5.30e-07 -10.0 1.66e-02 - 1.00e+00 1.00e+00h 1
16 -1.0125263e+00 6.43e-09 8.25e-08 -11.0 5.87e-03 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 16
(scaled) (unscaled)
Objective...............: -1.0125263394411863e+00 -1.0125263394411863e+00
Dual infeasibility......: 8.2471633885825539e-08 8.2471633885825539e-08
Constraint violation....: 1.8099727272513633e-09 6.4261801574483357e-09
Variable bound violation: 8.3128719602365564e-09 8.3128719602365564e-09
Complementarity.........: 6.6254607323533790e-10 6.6254607323533790e-10
Overall NLP error.......: 1.8099727272513633e-09 8.2471633885825539e-08
Number of objective function evaluations = 17
Number of objective gradient evaluations = 17
Number of equality constraint evaluations = 17
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 17
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 16
Total seconds in IPOPT = 0.230
EXIT: Optimal Solution Found.
BenchmarkTools.Trial: 19 samples with 1 evaluation.
Range (min … max): 233.690 ms … 614.121 ms ┊ GC (min … max): 0.00% … 55.63%
Time (median): 243.806 ms ┊ GC (median): 1.65%
Time (mean ± σ): 267.470 ms ± 84.652 ms ┊ GC (mean ± σ): 7.51% ± 12.59%
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234 ms Histogram: frequency by time 614 ms <
Memory estimate: 38.34 MiB, allocs estimate: 373614.
Necrothread :D Still, this is nice. We should add somewhere in the doc the steps to install HSL properly.
@ocots @PierreMartinon Can also try to use StaticArrays.jl, most probably in CTFlows.jl as CTDirect.jl are probably too large (size <= 100). Check https://github.com/JuliaArrays/StaticArrays.jl/issues/506