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Commit 2f5b8ec7 authored by Olivier Cots's avatar Olivier Cots
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Merge branch 'feature/final_bacteria_panel' into 'develop' 

Feature/final bacteria panel

See merge request !219
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2 merge requests!221Develop,!219Feature/final bacteria panel
This commit is part of merge request !221. Comments created here will be created in the context of that merge request.
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examples/bacteria/bacteria-panel.ipynb
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examples/bacteria/naiveproblem.def deleted 100644 → 0
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# Definition file
# Dimensions
dim.state 4
dim.control 1
dim.boundaryconditions 4
dim.pathconstraints 0
dim.parameters 0
dim.constants 4
# Time interval
initial.time 0
final.time 20
# Constants
constant.0 0.6
constant.1 0.003
constant.2 0.5
constant.3 0.07
# Time discretisation
ode.discretization midpoint_implicit
time.steps 200
# Bounds for constraints (initial conditions for our case)
boundarycond.0.lowerbound 0.03
boundarycond.0.upperbound 0.03
boundarycond.1.lowerbound 0.1
boundarycond.1.upperbound 0.1
boundarycond.2.lowerbound 0.2
boundarycond.2.upperbound 0.2
boundarycond.3.lowerbound 0.003
boundarycond.3.upperbound 0.003
# Bounds for variables (dynamical bound for each variable and control)
state.0.lowerbound 0
state.1.lowerbound 0
state.2.lowerbound 0
state.3.lowerbound 0
#Optimal value calculated for our initial conditions
control.0.lowerbound 0.4679139313552532
control.0.upperbound 0.4679139313552532
# Initialization for discretized problem (initial point for the optimization algorithm.. this we can touch only if the algorithm is not working, otherwise we can leave it like this)
state.0.init 0.1
state.1.init 0.1
state.2.init 0.1
state.3.init 0.1
control.0.init 0.4679139313552532
# Ipopt
ipoptIntOption.print_level 5
ipoptIntOption.max_iter 1000
ipoptStrOption.mu_strategy adaptive
ipoptNumOption.tol 1e-12
# Misc
ad.retape 0
examples/bacteria/problem1.def deleted 100644 → 0
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# Definition file
# Dimensions
dim.state 4
dim.control 1
dim.boundaryconditions 4
dim.pathconstraints 0
dim.parameters 0
dim.constants 4
# Time interval
initial.time 0
final.time 20
# Constants
constant.0 0.6
constant.1 0.003
constant.2 0.5
constant.3 0.07
# Time discretisation
ode.discretization midpoint_implicit
time.steps 200
# Bounds for constraints (initial conditions for our case)
boundarycond.0.lowerbound 0.03
boundarycond.0.upperbound 0.03
boundarycond.1.lowerbound 0.1
boundarycond.1.upperbound 0.1
boundarycond.2.lowerbound 0.2
boundarycond.2.upperbound 0.2
boundarycond.3.lowerbound 0.003
boundarycond.3.upperbound 0.003
# Bounds for variables (dynamical bound for each variable and control)
state.0.lowerbound 0
state.1.lowerbound 0
state.2.lowerbound 0
state.3.lowerbound 0
control.0.lowerbound 0
control.0.upperbound 1
# Initialization for discretized problem (initial point for the optimization algorithm.. this we can touch only if the algorithm is not working, otherwise we can leave it like this)
state.0.init 0.1
state.1.init 0.1
state.2.init 0.1
state.3.init 0.1
control.0.init 0.5
# Ipopt
ipoptIntOption.print_level 5
ipoptIntOption.max_iter 1000
ipoptStrOption.mu_strategy adaptive
ipoptNumOption.tol 1e-12
# Misc
ad.retape 0
examples/bacteria/result.out deleted 100644 → 0
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This is Ipopt version 3.12.12, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 72003
Number of nonzeros in inequality constraint Jacobian.: 1
Number of nonzeros in Lagrangian Hessian.............: 60000
Total number of variables............................: 18004
variables with only lower bounds: 8004
variables with lower and upper bounds: 2000
variables with only upper bounds: 0
Total number of equality constraints.................: 16003
Total number of inequality constraints...............: 1
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 -1.0000000e-01 9.70e-02 2.00e+00 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 -1.7956913e-01 6.45e-02 4.01e+01 -6.6 1.48e+00 - 1.87e-01 3.35e-01h 1
2 -1.9122520e-01 6.54e-02 2.65e+01 -2.4 8.53e-02 - 6.94e-01 3.91e-01h 1
3 -1.6529180e-01 5.74e-02 5.40e+01 -1.8 6.97e-02 2.0 1.00e+00 3.72e-01h 1
4 -2.1815243e-01 8.26e-02 1.96e+03 -0.5 5.38e-01 1.5 9.14e-01 1.00e+00f 1
5 -2.1815144e-03 2.71e-02 5.24e+02 -0.7 3.37e-01 - 7.22e-01 7.62e-01h 1
6 -2.5313633e-02 3.94e-03 2.88e+02 -1.2 1.20e-01 - 6.21e-01 1.00e+00f 1
7 -2.4639386e-02 3.84e-05 4.36e+00 -6.9 7.66e-03 - 9.39e-01 1.00e+00h 1
8 -2.4664593e-02 9.12e-08 2.26e-02 -8.1 7.46e-04 - 9.89e-01 1.00e+00h 1
9 -2.6949507e-02 8.25e-04 2.77e-03 -10.1 6.61e-02 - 8.77e-01 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 -3.6248795e-02 9.94e-03 2.68e-03 -11.0 7.95e+00 - 3.56e-02 8.75e-02h 1
11 -4.4605068e-02 1.04e-02 2.46e-03 -11.0 4.16e+00 - 9.72e-02 9.57e-02h 1
12 -5.2348837e-02 1.15e-02 2.23e-03 -11.1 3.97e+00 - 1.01e-01 1.20e-01h 1
13 -5.9959503e-02 1.10e-02 1.99e-03 -11.1 3.70e+00 - 1.31e-01 1.08e-01h 1
14 -6.8960594e-02 1.03e-02 1.88e-03 -11.2 3.81e+00 - 1.10e-01 1.17e-01h 1
15 -7.7873181e-02 9.66e-03 1.92e-03 -11.2 4.99e+00 - 9.16e-02 8.44e-02h 1
16 -8.1380349e-02 9.38e-03 1.80e-03 -7.0 3.36e+00 - 1.31e-01 3.07e-02h 1
17 -8.1435434e-02 9.34e-03 1.81e-01 -6.0 2.86e-01 - 1.00e+00 3.94e-03h 1
18 -8.9640050e-02 2.74e-02 1.56e-03 -6.6 2.89e-01 - 1.00e+00 1.00e+00h 1
19 -8.6499553e-02 4.74e-03 3.44e-04 -6.2 7.83e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 -8.6525808e-02 2.69e-03 1.55e-04 -6.7 1.44e-01 - 1.00e+00 1.00e+00h 1
21 -8.6452574e-02 1.19e-03 6.74e-05 -7.3 2.75e-01 - 1.00e+00 1.00e+00h 1
22 -8.6345131e-02 3.60e-04 1.54e-05 -8.0 2.26e-01 - 1.00e+00 1.00e+00h 1
23 -8.6322414e-02 3.12e-04 1.03e-06 -8.2 3.23e-01 - 1.00e+00 1.00e+00h 1
24 -8.6318619e-02 9.61e-05 9.94e-08 -7.9 2.70e-01 - 1.00e+00 1.00e+00h 1
25 -8.6323512e-02 1.04e-04 4.03e-07 -8.7 1.81e-01 - 9.90e-01 1.00e+00h 1
26 -8.6324213e-02 1.15e-04 1.70e-07 -8.9 2.80e-01 - 1.00e+00 1.00e+00h 1
27 -8.6324506e-02 4.36e-05 9.15e-08 -9.1 2.74e-01 - 9.69e-01 1.00e+00h 1
28 -8.6324819e-02 3.68e-05 2.11e-07 -9.4 2.03e-01 - 9.69e-01 1.00e+00h 1
29 -8.6324970e-02 4.95e-05 1.78e-08 -9.9 3.97e-01 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
30 -8.6325013e-02 4.03e-05 1.65e-08 -10.2 1.95e-01 - 9.87e-01 1.00e+00h 1
31 -8.6325037e-02 2.38e-05 2.39e-09 -10.7 1.69e-01 - 9.96e-01 1.00e+00h 1
32 -8.6325047e-02 3.09e-05 2.23e-09 -11.3 3.19e-01 - 9.95e-01 1.00e+00h 1
33 -8.6325048e-02 1.59e-05 1.06e-09 -11.5 1.78e-01 - 1.00e+00 1.00e+00h 1
34 -8.6325049e-02 3.42e-06 1.10e-09 -11.8 1.18e-01 - 9.89e-01 1.00e+00h 1
35 -8.6325050e-02 5.44e-06 2.72e-10 -12.3 1.13e-01 - 1.00e+00 1.00e+00h 1
36 -8.6325050e-02 1.08e-06 1.88e-11 -12.3 6.74e-02 - 1.00e+00 1.00e+00h 1
37 -8.6325050e-02 6.78e-09 3.53e-14 -12.3 6.50e-03 - 1.00e+00 1.00e+00h 1
38 -8.6325050e-02 1.64e-13 3.36e-15 -12.3 4.01e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 38
(scaled) (unscaled)
Objective...............: -8.6325049991363728e-02 -8.6325049991363728e-02
Dual infeasibility......: 3.3628966967052720e-15 3.3628966967052720e-15
Constraint violation....: 1.6396606294932781e-13 1.6396606294932781e-13
Complementarity.........: 5.0000283212168177e-13 5.0000283212168177e-13
Overall NLP error.......: 5.0000283212168177e-13 5.0000283212168177e-13
Number of objective function evaluations = 39
Number of objective gradient evaluations = 39
Number of equality constraint evaluations = 39
Number of inequality constraint evaluations = 39
Number of equality constraint Jacobian evaluations = 39
Number of inequality constraint Jacobian evaluations = 39
Number of Lagrangian Hessian evaluations = 38
Total CPU secs in IPOPT (w/o function evaluations) = 14.993
Total CPU secs in NLP function evaluations = 42.832
EXIT: Optimal Solution Found.
examples/swimmer/problem_1.ipynb 0 → 100644
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