pyanno4rt.optimization.solvers._proxmin_solver

Proxmin wrapper.

Overview

Classes

ProxminSolver

Proxmin wrapper class.

Classes

class pyanno4rt.optimization.solvers._proxmin_solver.ProxminSolver(number_of_variables, number_of_constraints, problem_instance, lower_variable_bounds, upper_variable_bounds, lower_constraint_bounds, upper_constraint_bounds, algorithm, initial_fluence, max_iter, tolerance)

Proxmin wrapper class.

This class serves as a wrapper for the proximal optimization algorithms from the Proxmin solver. It takes the problem structure, configures the selected algorithm, and defines the method to run the solver.

Parameters:

• number_of_variables (int) – Number of decision variables.

• number_of_constraints (int) – Number of constraints.

• problem_instance (object of class LexicographicOptimization WeightedSumOptimization) – The object representing the optimization problem.

• lower_variable_bounds (list) – Lower bounds on the decision variables.

• upper_variable_bounds (list) – Upper bounds on the decision variables.

• lower_constraint_bounds (list) – Lower bounds on the constraints.

• upper_constraint_bounds (list) – Upper bounds on the constraints.

• algorithm (str) – Label for the solution algorithm.

• initial_fluence (ndarray) – Initial fluence vector.

• max_iter (int) – Maximum number of iterations.

• tolerance (float)

• value. (Precision goal for the objective function)

fun

Minimization function from the Proxmin library.

Type:

callable

arguments

Dictionary with the function arguments.

Type:

dict

Overview

Methods

callback(X, it, objective)	Log the intermediate results after each iteration.
run(initial_fluence)	Run the Proxmin solver.

Members

callback(X, it, objective)

Log the intermediate results after each iteration.

Parameters:

• X (ndarray) – Optimal point of the current iteration.

• it (int) – Iteration counter.

• fun (callable) – Objective value function.

run(initial_fluence)

Run the Proxmin solver.

Parameters:

initial_fluence (ndarray) – Initial fluence vector.

Returns:

• ndarray – Optimized fluence vector.

• str – Description for the cause of termination.