## Main.OptionApmSolver History

August 30, 2020, at 12:13 PM by 136.36.211.159 -
Changed line 84 from:

The selection of the linear solver in IPOPT affects the speed and sometimes the ability of IPOPT to solve the problem, especially for ill-conditioned problems. IPOPT is available through the default public server. However, IPOPT and certain linear solvers are not currently available (track issue on GitHub) for local solution without an internet connection.

to:

The selection of the linear solver in IPOPT affects the speed and sometimes the ability of IPOPT to solve the problem, especially for ill-conditioned problems. IPOPT is available through the default public server. However, IPOPT and certain linear solvers are not currently available (track issue on GitHub) for local solution without an internet connection.

August 30, 2020, at 11:53 AM by 136.36.211.159 -
Changed line 35 from:

m = GEKKO(server='https://byu.apmonitor.com')

to:

m = GEKKO(remote=True,server='https://byu.apmonitor.com')

Changed line 41 from:

m = GEKKO(server='https://byu.apmonitor.com')

to:

m = GEKKO(remote=True,server='https://byu.apmonitor.com')

August 30, 2020, at 11:52 AM by 136.36.211.159 -
Changed lines 74-94 from:

Refer to Options for ipopt.opt for additional details for the IPOPT solver

to:

Refer to Options for ipopt.opt for additional details for the IPOPT solver. The IPOPT solver links to free and open-source (e.g. MUMPS) linear solvers or others that require a license (e.g. HSL MA57).

(:source lang=python:) from gekko import GEKKO m = GEKKO(remote=True) m.options.SOLVER=3 # Select IPOPT m.solver_options = ['linear_solver ma57', 'mu_strategy adaptive'] (:sourceend:)

The selection of the linear solver in IPOPT affects the speed and sometimes the ability of IPOPT to solve the problem, especially for ill-conditioned problems. IPOPT is available through the default public server. However, IPOPT and certain linear solvers are not currently available (track issue on GitHub) for local solution without an internet connection.

(:source lang=python:) from gekko import GEKKO m = GEKKO(remote=False) m.options.SOLVER=3 # Select IPOPT m.solver_options = ['linear_solver mumps', 'mu_strategy adaptive'] (:sourceend:)

When switching to a local solution, the solver selection will switch to one that is available (e.g. APOPT) or else use IPOPT but with a linear solver that can be distributed freely. This change of solvers sometimes leads to a different solution when using remote=False.

April 10, 2020, at 05:24 AM by 136.36.211.159 -
Changed line 74 from:

Refer to Options for ipopt.opt for additional details for the IPOPT solver

to:

Refer to Options for ipopt.opt for additional details for the IPOPT solver

September 20, 2018, at 12:44 AM by 173.127.175.118 -
Changed line 60 from:

$$gap=\frac{obj_i-obj_r}{\max(\frac{\|obj_i\|+\|obj_r\|}{2},1)}$$

to:

$$gap=\frac{obj_i-obj_r}{\max \left( \frac{\lvert obj_i\rvert+\lvert obj_r\rvert}{2},1 \right)}$$

September 20, 2018, at 12:40 AM by 173.127.175.118 -
Changed line 60 from:

$$gap=\frac{obj_i-obj_r}{\max(\frac{\abs(obj_i)+\abs(obj_r)}{2},1)}$$

to:

$$gap=\frac{obj_i-obj_r}{\max(\frac{\|obj_i\|+\|obj_r\|}{2},1)}$$

September 20, 2018, at 12:39 AM by 173.127.175.118 -
Changed line 60 from:

$$gap=\frac{obj_i-obj_r}{\max((abs(obj_i)+abs(obj_r))/2,1)}$$

to:

$$gap=\frac{obj_i-obj_r}{\max(\frac{\abs(obj_i)+\abs(obj_r)}{2},1)}$$

September 20, 2018, at 12:37 AM by 173.127.175.118 -
Changed lines 58-61 from:
• minlp_gap_tol 1.0e-2 - gap is the spread between the lowest candidate leaf (obj_r=non-integer solution) and the best integer solution (obj_i). When the gap is below the minlp_gap_tol, the best integer solution is returned. The gap is defined as gap=(obj_i-obj_r)/max((abs(obj_i)+abs(obj_r))/2,1).
to:
• minlp_gap_tol 1.0e-2 - gap is the spread between the lowest candidate leaf (obj_r=non-integer solution) and the best integer solution (obj_i). When the gap is below the minlp_gap_tol, the best integer solution is returned. The gap is defined as

$$gap=\frac{obj_i-obj_r}{\max((abs(obj_i)+abs(obj_r))/2,1)}$$

September 20, 2018, at 12:27 AM by 173.127.175.118 -
Changed line 71 from:

Refer toOptions for ipopt.opt for additional details for the IPOPT solver

to:

Refer to Options for ipopt.opt for additional details for the IPOPT solver

September 20, 2018, at 12:27 AM by 173.127.175.118 -
Deleted lines 31-32:

Refer to Options for apopt.opt for additional details for the APOPT solver and Options for ipopt.opt for additional details for the IPOPT solver.

Refer to Options for apopt.opt for additional details for the APOPT solver.

Refer toOptions for ipopt.opt for additional details for the IPOPT solver

September 20, 2018, at 12:25 AM by 173.127.175.118 -
Changed line 55 from:
• minlp_maximum_iterations 10000 - maximum number of nlp solutions from the branch and bound method. A successful solution is returned if there is an integer solution upon reaching the maximum number of iterations. Otherwise, the solution is not considered to be successful and an error message is returned with the failed solution.
to:
• minlp_maximum_iterations 10000 - maximum number of nlp solutions from the branch and bound method. A successful solution is returned if there is an integer solution upon reaching the maximum number of iterations. Otherwise, the solution is not considered to be successful and an error message is returned with the failed solution.

#### IPOPT Options

September 20, 2018, at 12:25 AM by 173.127.175.118 -

#### APOPT Options

The APOPT solver has a number of options that are available for tuning the solver performance. Some of the available options and default values are listed below:

• minlp_maximum_iterations 10000 - maximum number of nlp solutions from the branch and bound method. A successful solution is returned if there is an integer solution upon reaching the maximum number of iterations. Otherwise, the solution is not considered to be successful and an error message is returned with the failed solution.
• minlp_max_iter_with_int_sol 500 - maximum number of nlp solutions when a candidate integer solution is found
• minlp_as_nlp 1 - solve minlp problem as a continuous nlp problem, ignoring integer constraints
• minlp_branch_method 3 - 1=depth first (find integer solution faster), 2=breadth first, 3=lowest objective leaf, 4=highest objective leaf
• minlp_gap_tol 1.0e-2 - gap is the spread between the lowest candidate leaf (obj_r=non-integer solution) and the best integer solution (obj_i). When the gap is below the minlp_gap_tol, the best integer solution is returned. The gap is defined as gap=(obj_i-obj_r)/max((abs(obj_i)+abs(obj_r))/2,1).
• minlp_integer_tol 1.0e-2 - amount that a candidate solution variable can deviate from an integer solution and still be considered an integer.
• minlp_integer_max 2.0e9 - maximum value to be considered as an integer. Values over 2147483647 or below -2147483648 not stored correctly with an internal integer variable type because of the number of bits used to store an integer.
• minlp_integer_leaves 1 - add additional integer leaves, 0=off, 1=integer leaves with inequality on branching, 2=integer leaves with equality constraint on branching.
• minlp_print_level 1 - print level (0-10). Development version has additional advanced diagnostics.
• nlp_maximum_iterations 500 - maximum number of iterations for each nlp sub-problem. Reducing the nlp maximum iterations can improve the solution speed because less computational time is spent on candidate solutions that may not converge
• objective_convergence_tolerance 1.0e-6 - convergence tolerance for the objective function. Values lower than 1.0e-10 sometimes run into covergence problems because of numerical scaling and cannot achieve the requested accuracy.
• constraint_convergence_tolerance 1.0e-6 - convergence tolerance for the constraints. A lower convergence tolerance typically adds only a couple additional iterations to the solution but the solution also does not change significantly.
September 19, 2018, at 11:44 PM by 173.127.175.118 -

(:sourceend:)

(:source lang=python:) m = GEKKO(server='https://byu.apmonitor.com')

1. multiple options as one list

m.solver_options = ['minlp_gap_tol 1.0e-2', 'minlp_maximum_iterations 10000', 'minlp_max_iter_with_int_sol 500'] m.options.solver = 1

In the GEKKO Optimization Suite the solver options are changed directly within Python.

(:source lang=python:) m = GEKKO(server='https://byu.apmonitor.com') m.solver_options = ['linear_solver ma57'] m.options.solver = 3 (:sourceend:)

Changed line 32 from:

Refer to Options for apopt.opt for additional details for the APOPT solver and Options for ipopt.opt file for additional details for the IPOPT solver.

to:

Refer to Options for apopt.opt for additional details for the APOPT solver and Options for ipopt.opt for additional details for the IPOPT solver.

Options for solvers can be set using configuration parameters such as MAX_ITER or else by creating an options file such as ipopt.opt or apopt.opt. Below is an example of setting options for the APOPT solver for a mixed integer nonlinear programming solution. The File...End File section creates a new apopt.opt file when APMonitor compiles the model file.

 File apopt.opt
minlp_maximum_iterations 10000
minlp_max_iter_with_int_sol 500
minlp_as_nlp 1
minlp_branch_method 3
minlp_gap_tol 1.0e-2
minlp_integer_tol 1.0e-2
minlp_integer_max 2.0e9
minlp_integer_leaves 1
minlp_print_level 1
nlp_maximum_iterations 500
objective_convergence_tolerance 1.0e-6
constraint_convergence_tolerance 1.0e-6
End File


Refer to Options for apopt.opt for additional details for the APOPT solver and Options for ipopt.opt file for additional details for the IPOPT solver.

June 09, 2017, at 12:47 AM by 10.5.113.159 -
Changed lines 5-6 from:
to:

June 01, 2017, at 07:52 PM by 45.56.3.173 -
 Type: Integer, Input