APM.SOLVER - APMonitor Option

Main

APM.SOLVER - APMonitor Option

Global Options |

Local Options

 Type: Integer, Input
 Default Value: 3
 Description: Solver options
              0 = Benchmark All Solvers
              1-5 = Available Solvers Depends on License

SOLVER selects the solver to use in an attempt to find a solution. There are free solvers: 1: APOPT, 2: BPOPT, 3: IPOPT distributed with the public version of the software. There are additional solvers that are not included with the public version and require a commercial license. IPOPT is generally the best for problems with large numbers of degrees of freedom or when starting without a good initial guess. BPOPT has been found to be the best for systems biology applications. APOPT is generally the best when warm-starting from a prior solution or when the number of degrees of freedom (Number of Variables - Number of Equations) is less than 2000. APOPT is also the only solver that handles Mixed Integer problems. Use option 0 to compare all available solvers.

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

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

m = GEKKO(server='http://byu.apmonitor.com')
m.solver_options = ['linear_solver ma57']
m.options.solver = 3

[$[Get Code]]

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

[$[Get Code]]

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

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

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.

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

IPOPT Options

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

APMonitor Documentation