pyanno4rt.optimization.solvers

Solvers module.

This module aims to provide methods and classes for wrapping the local and global solution algorithms from the integrated optimization packages.

Subpackages

Overview

Classes
`ProxminSolver`	Proxmin wrapper class.
`Pyanno4rtSolver`	Internal wrapper class.
`PymooSolver`	Pymoo wrapper class.
`PyPop7Solver`	PyPop7 wrapper class.
`SciPySolver`	SciPy wrapper class.

Attributes
`solver_map`	-

Classes

class pyanno4rt.optimization.solvers.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)[source]

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)[source]

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)[source]

Run the Proxmin solver.

Parameters:

initial_fluence (ndarray) – Initial fluence vector.

Returns:

ndarray – Optimized fluence vector.
str – Description for the cause of termination.

class pyanno4rt.optimization.solvers.Pyanno4rtSolver(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)[source]

Internal wrapper class.

This class serves as a wrapper for the internal optimization algorithms. 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) – Precision goal for the objective function value.

fun

Minimization function from the pyanno4rt library.

Type:: callable

arguments

Dictionary with the function arguments.

Type:: dict

Overview

Methods
`run`(initial_fluence)	Run the pyanno4rt solver.

Members

run(initial_fluence)[source]

Run the pyanno4rt solver.

Parameters:

initial_fluence (ndarray) – Initial fluence vector.

Returns:

ndarray – Optimized fluence vector.
str – Description for the cause of termination.

class pyanno4rt.optimization.solvers.PymooSolver(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)[source]

Pymoo wrapper class.

This class serves as a wrapper for the multi-objective (Pareto) optimization algorithms from the Pymoo 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 ParetoOptimization The object representing the (Pareto) 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) – Precision goal for the objective function value.

fun

Minimization function from the Pymoo library.

Type:: callable

algorithm_object

The object representing the solution algorithm.

Type:: object of class from pymoo.algorithms

problem

The object representing the Pymoo-compatible structure of the multi-objective (Pareto) optimization problem.

Type:: object of class from pymoo.core.problem

termination

The object representing the termination criterion.

Type:: object of class from pymoo.termination

Overview

Methods
`run`(_)	Run the Pymoo solver.

Members

run(_)[source]

Run the Pymoo solver.

Parameters:

initial_fluence (ndarray) – Initial fluence vector.

Returns:

ndarray – Optimized (Pareto) set of fluence vectors.
str – Description for the cause of termination.

class pyanno4rt.optimization.solvers.PyPop7Solver(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)[source]

PyPop7 wrapper class.

This class serves as a wrapper for the population-based optimization algorithms from the PyPop7 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) – Precision goal for the objective function value.

fun

The object representing the optimization algorithm.

Type:: object

arguments

Dictionary with the function arguments.

Type:: dict

Overview

Methods
`run`(initial_fluence)	Run the PyPop7 solver.

Members

run(initial_fluence)[source]

Run the PyPop7 solver.

Parameters:

initial_fluence (ndarray) – Initial fluence vector.

Returns:

ndarray – Optimized fluence vector.
str – Description for the cause of termination.

class pyanno4rt.optimization.solvers.SciPySolver(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)[source]

SciPy wrapper class.

This class serves as a wrapper for the local optimization algorithms from the SciPy 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) – Precision goal for the objective function value.

fun

Minimization function from the SciPy library.

Type:: callable

arguments

Dictionary with the function arguments.

Type:: dict

counter

Counter for the iterations.

Type:: int

Overview

Methods
`callback`(intermediate_result)	Log the intermediate results after each iteration.
`run`(initial_fluence)	Run the SciPy solver.

Members

callback(intermediate_result)[source]

Log the intermediate results after each iteration.

Parameters:: intermediate_result (dict) – Dictionary with the intermediate results of the current iteration.

run(initial_fluence)[source]

Run the SciPy solver.

Parameters:

initial_fluence (ndarray) – Initial fluence vector.

Returns:

ndarray – Optimized fluence vector.
str – Description for the cause of termination.

Attributes

pyanno4rt.optimization.solvers.solver_map