InteriorPoint

Options table

Option	Type	Default Value	Description
problem	Problem or ProblemLite	None	Object containing the problem to be solved.
recording	bool	False	If True, record all outputs from the optimization. This needs to be enabled for hot-starting the same problem later, if the optimization is interrupted.
out_dir	str	None	The directory to store all the output files generated from the optimization.
hot_start_from	str	None	The record file from which to hot-start the optimization.
hot_start_atol	float	0.	The absolute tolerance check for the inputs when reusing outputs from the hot-start record.
hot_start_rtol	float	0.	The relative tolerance check for the inputs when reusing outputs from the hot-start record.
visualize	list	[]	The list of scalar variables to visualize during the optimization.
keep_viz_open	bool	False	If True, keeps the visualization window open after the optimization is complete.
turn_off_outputs	bool	False	If True, prevent modOpt from generating any output files.
maxiter	int	1000	Maximum number of major iterations.
opt_tol	float	1e-6	Optimality tolerance.
feas_tol	float	1e-6	Feasibility tolerance.
fraction_to_boundary	float	0.99	Fraction to the boundary for step size calculation.
init_barrier_param	float	0.50	Initial value of the barrier parameter.
barrier_reduction_factor	float	0.20	Factor by which the barrier parameter is reduced when convergence tolerances are met for the current barrier problem.
barrier_reduction_power	float	1.50	Power to which the barrier parameter is raised when convergence tolerances are met for the current barrier problem.
barrier_problem_tolerance_factor	float	10.0	Convergence tolerance for each barrier problem set as a factor of the current barrier parameter.
ls_maxiter	int	10	Maximum number of line search iterations.
ls_eta_a	float	1e-4	Armijo parameter for the line search.
ls_gamma_c	float	0.5	Step length contraction factor for backtracking line search.
ls_max_step	float	1.0	Maximum step length for line search.
bfgs_reset_frequency	int	100	Iteration frequency to reset the BFGS approximate Hessian.
readable_outputs	list	[]	List of outputs to be written to readable text output files. Available outputs are: ‘major’, ‘obj’, ‘x’, ‘lag_mult’, ‘slacks’, ‘constraints’, ‘opt’, ‘feas’, ‘time’, ‘nfev’, ‘ngev’, ‘step’, ‘rho’, ‘merit’.

Source code

The source code is available here.

API

class modopt.InteriorPoint(problem, recording=False, out_dir=None, hot_start_from=None, hot_start_atol=0.0, hot_start_rtol=0.0, visualize=[], keep_viz_open=False, turn_off_outputs=False, **kwargs)

An Interior Point algorithm for nonlinearly constrained optimization problems.

Parameters

problemProblem or ProblemLite

Object containing the problem to be solved.

recordingbool, default=False

If True, record all outputs from the optimization. This needs to be enabled for hot-starting the same problem later, if the optimization is interrupted.

out_dirstr, optional

The directory to store all the output files generated from the optimization.

hot_start_fromstr, optional

The record file from which to hot-start the optimization.

hot_start_atolfloat, default=0.

The absolute tolerance check for the inputs when reusing outputs from the hot-start record.

hot_start_rtolfloat, default=0.

The relative tolerance check for the inputs when reusing outputs from the hot-start record.

visualizelist, default=[]

The list of scalar variables to visualize during the optimization.

keep_viz_openbool, default=False

If True, keeps the visualization window open after the optimization is complete.

turn_off_outputsbool, default=False

If True, prevent modOpt from generating any output files.

maxiterint, default=1000

Maximum number of major iterations.

opt_tolfloat, default=1e-6

Optimality tolerance.

feas_tolfloat, default=1e-6

Feasibility tolerance.

fraction_to_boundaryfloat, default=0.99

Fraction to the boundary for step size calculation.

init_barrier_paramfloat, default=0.50

Initial value of the barrier parameter.

barrier_reduction_factorfloat, default=0.20

Factor by which the barrier parameter is reduced when convergence tolerances are met for the current barrier problem.

barrier_reduction_powerfloat, default=1.50

Power to which the barrier parameter is raised when convergence tolerances are met for the current barrier problem.

barrier_problem_tolerance_factorfloat, default=10.0

Convergence tolerance for each barrier problem set as a factor of the current barrier parameter.

ls_maxiterint, default=10

Maximum number of line search iterations.

ls_eta_afloat, default=1e-4

Armijo parameter for the line search.

ls_gamma_cfloat, default=0.5

Step length contraction factor for backtracking line search.

ls_max_stepfloat, default=1.0

Maximum step length for line search.

bfgs_reset_frequencyint, default=100

Iteration frequency to reset the BFGS approximate Hessian.

readable_outputslist, default=[]

List of outputs to be written to readable text output files. Available outputs are: ‘major’, ‘obj’, ‘x’, ‘lag_mult’, ‘slacks’, ‘constraints’, ‘opt’, ‘feas’, ‘time’, ‘nfev’, ‘ngev’, ‘step’, ‘rho’, ‘merit’.

Methods

check_first_derivatives([x, step, formulation])	Check the first derivatives of the optimization problem using finite differences.
print_results([summary_table, all])	Print the results of the optimization problem to the terminal.
solve()	Run the optimization algorithm to solve the problem.

check_first_derivatives(x=None, step=1e-06, formulation='rs')

Check the first derivatives of the optimization problem using finite differences.

Parameters

xnp.ndarray, optional

The design variables at which the derivatives are to be checked. If not provided, the initial design variables are used.

stepfloat, default=1e-6

The step size for the finite differences.

print_results(summary_table=False, all=False)

Print the results of the optimization problem to the terminal.

Parameters

summary_tablebool, default=False

If True, print the summary table for the optimization.

allbool, default=False

If False, print only the scalar outputs of the optimization problem. Otherwise, print all the outputs of the optimization problem.

solve()

Run the optimization algorithm to solve the problem.