simopt.solvers package

Submodules

simopt.solvers.adam module

Summary

ADAM An algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. A detailed description of the solver can be found here.

class simopt.solvers.adam.ADAM(name='ADAM', fixed_factors=None)

Bases: Solver

An algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments.

name

name of solver

Type:

string

objective_type
description of objective types:

“single” or “multi”

Type:

string

constraint_type
description of constraints types:

“unconstrained”, “box”, “deterministic”, “stochastic”

Type:

string

variable_type
description of variable types:

“discrete”, “continuous”, “mixed”

Type:

string

gradient_needed

indicates if gradient of objective function is needed

Type:

bool

factors

changeable factors (i.e., parameters) of the solver

Type:

dict

specifications

details of each factor (for GUI, data validation, and defaults)

Type:

dict

rng_list

list of RNGs used for the solver’s internal purposes

Type:

list of mrg32k3a.mrg32k3a.MRG32k3a objects

Parameters:

  • name (str) – user-specified name for solver

  • fixed_factors (dict) – fixed_factors of the solver

See also

base.Solver

check_alpha()
check_beta_1()
check_beta_2()
check_epsilon()
check_r()
check_sensitivity()
finite_diff(new_solution, BdsCheck, problem)
solve(problem)

Run a single macroreplication of a solver on a problem.

Parameters:

  • problem (Problem object) – simulation-optimization problem to solve

  • crn_across_solns (bool) – indicates if CRN are used when simulating different solutions

Returns:

  • recommended_solns (list of Solution objects) – list of solutions recommended throughout the budget

  • intermediate_budgets (list of ints) – list of intermediate budgets when recommended solutions changes

simopt.solvers.aloe module

Summary

ALOE The solver is a stochastic line search algorithm with the gradient estimate recomputed in each iteration, whether or not a step is accepted. The algorithm includes the relaxation of the Armijo condition by an additive constant. A detailed description of the solver can be found here.

class simopt.solvers.aloe.ALOE(name='ALOE', fixed_factors=None)

Bases: Solver

Adaptive Line-search with Oracle Estimations

name

name of solver

Type:

string

objective_type
description of objective types:

“single” or “multi”

Type:

string

constraint_type
description of constraints types:

“unconstrained”, “box”, “deterministic”, “stochastic”

Type:

string

variable_type
description of variable types:

“discrete”, “continuous”, “mixed”

Type:

string

gradient_needed

indicates if gradient of objective function is needed

Type:

bool

factors

changeable factors (i.e., parameters) of the solver

Type:

dict

specifications

details of each factor (for GUI, data validation, and defaults)

Type:

dict

rng_list

list of RNGs used for the solver’s internal purposes

Type:

list of mrg32k3a.mrg32k3a.MRG32k3a objects

Parameters:

  • name (str) – user-specified name for solver

  • fixed_factors (dict) – fixed_factors of the solver

See also

base.Solver

check_alpha_0()
check_alpha_max()
check_epsilon_f()
check_gamma()
check_lambda()
check_r()
check_sensitivity()
check_theta()
finite_diff(new_solution, BdsCheck, problem, stepsize, r)
solve(problem)

Run a single macroreplication of a solver on a problem.

Parameters:

  • problem (Problem object) – simulation-optimization problem to solve

  • crn_across_solns (bool) – indicates if CRN are used when simulating different solutions

Returns:

  • recommended_solns (list of Solution objects) – list of solutions recommended throughout the budget

  • intermediate_budgets (list of ints) – list of intermediate budgets when recommended solutions changes

simopt.solvers.astrodf module

Summary

The ASTRO-DF solver progressively builds local models (quadratic with diagonal Hessian) using interpolation on a set of points on the coordinate bases of the best (incumbent) solution. Solving the local models within a trust region (closed ball around the incumbent solution) at each iteration suggests a candidate solution for the next iteration. If the candidate solution is worse than the best interpolation point, it is replaced with the latter (a.k.a. direct search). The solver then decides whether to accept the candidate solution and expand the trust-region or reject it and shrink the trust-region based on a success ratio test. The sample size at each visited point is determined adaptively and based on closeness to optimality. A detailed description of the solver can be found here.

This version does not require a delta_max, instead it estimates the maximum step size using get_random_solution(). Parameter tuning on delta_max is therefore not needed and removed from this version as well. - Delta_max is so longer a factor, instead the maximum step size is estimated using get_random_solution(). - Parameter tuning on delta_max is therefore not needed and removed from this version as well. - No upper bound on sample size may be better - testing - It seems for SAN we always use pattern search - why? because the problem is convex and model may be misleading at the beginning - Added sufficient reduction for the pattern search

class simopt.solvers.astrodf.ASTRODF(name='ASTRODF', fixed_factors=None)

Bases: Solver

The ASTRO-DF solver.

name

name of solver

Type:

string

objective_type
description of objective types:

“single” or “multi”

Type:

string

constraint_type
description of constraints types:

“unconstrained”, “box”, “deterministic”, “stochastic”

Type:

string

variable_type
description of variable types:

“discrete”, “continuous”, “mixed”

Type:

string

gradient_needed

indicates if gradient of objective function is needed

Type:

bool

factors

changeable factors (i.e., parameters) of the solver

Type:

dict

specifications

details of each factor (for GUI, data validation, and defaults)

Type:

dict

rng_list

list of RNGs used for the solver’s internal purposes

Type:

list of mrg32k3a.mrg32k3a.MRG32k3a objects

Parameters:

  • name (str) – user-specified name for solver

  • fixed_factors (dict) – fixed_factors of the solver

See also

base.Solver

check_eta_1()
check_eta_2()
check_gamma_1()
check_gamma_2()
check_lambda_min()
check_ps_sufficient_reduction()
construct_model(x_k, delta, k, problem, expended_budget, kappa, new_solution, visited_pts_list)
evaluate_model(x_k, q)
get_coordinate_basis_interpolation_points(x_k, delta, problem)
get_coordinate_vector(size, v_no)
get_model_coefficients(Y, fval, problem)
get_rotated_basis(first_basis, rotate_index)
get_rotated_basis_interpolation_points(x_k, delta, problem, rotate_matrix, reused_x)
get_stopping_time(k, sig2, delta, kappa, dim)
iterate(k, delta_k, delta_max, problem, visited_pts_list, new_x, expended_budget, budget_limit, recommended_solns, intermediate_budgets, kappa, new_solution)
solve(problem)

Run a single macroreplication of a solver on a problem. :param problem: simulation-optimization problem to solve :type problem: Problem object :param crn_across_solns: indicates if CRN are used when simulating different solutions :type crn_across_solns: bool

Returns:

  • recommended_solns (list of Solution objects) – list of solutions recommended throughout the budget

  • intermediate_budgets (list of ints) – list of intermediate budgets when recommended solutions changes

simopt.solvers.neldmd module

Summary

Nelder-Mead: An algorithm that maintains a simplex of points that moves around the feasible region according to certain geometric operations: reflection, expansion, contraction, and shrinking. A detailed description of the solver can be found here.

class simopt.solvers.neldmd.NelderMead(name='NELDMD', fixed_factors=None)

Bases: Solver

The Nelder-Mead algorithm, which maintains a simplex of points that moves around the feasible region according to certain geometric operations: reflection, expansion, contraction, and shrinking.

name

name of solver

Type:

string

objective_type
description of objective types:

“single” or “multi”

Type:

string

constraint_type
description of constraints types:

“unconstrained”, “box”, “deterministic”, “stochastic”

Type:

string

variable_type
description of variable types:

“discrete”, “continuous”, “mixed”

Type:

string

gradient_needed

indicates if gradient of objective function is needed

Type:

bool

factors

changeable factors (i.e., parameters) of the solver

Type:

dict

specifications

details of each factor (for GUI, data validation, and defaults)

Type:

dict

rng_list

list of RNGs used for the solver’s internal purposes

Type:

list of mrg32k3a.mrg32k3a.MRG32k3a objects

Parameters:

  • name (str) – user-specified name for solver

  • fixed_factors (dict) – fixed_factors of the solver

See also

base.Solver

check_alpha()
check_betap()
check_const(pt, pt2)
check_delta()
check_gammap()
check_initial_spread()
check_r()
check_sensitivity()
solve(problem)

Run a single macroreplication of a solver on a problem.

Parameters:

problem (Problem object) – simulation-optimization problem to solve

Returns:

  • recommended_solns (list of Solution objects) – list of solutions recommended throughout the budget

  • intermediate_budgets (list of ints) – list of intermediate budgets when recommended solutions changes

sort_and_end_update(problem, sol)

simopt.solvers.randomsearch module

Summary

Randomly sample solutions from the feasible region. Can handle stochastic constraints. A detailed description of the solver can be found here.

class simopt.solvers.randomsearch.RandomSearch(name='RNDSRCH', fixed_factors=None)

Bases: Solver

A solver that randomly samples solutions from the feasible region. Take a fixed number of replications at each solution.

name

name of solver

Type:

string

objective_type
description of objective types:

“single” or “multi”

Type:

string

constraint_type
description of constraints types:

“unconstrained”, “box”, “deterministic”, “stochastic”

Type:

string

variable_type
description of variable types:

“discrete”, “continuous”, “mixed”

Type:

string

gradient_needed

indicates if gradient of objective function is needed

Type:

bool

factors

changeable factors (i.e., parameters) of the solver

Type:

dict

specifications

details of each factor (for GUI, data validation, and defaults)

Type:

dict

rng_list

list of RNGs used for the solver’s internal purposes

Type:

list of mrg32k3a.mrg32k3a.MRG32k3a objects

Parameters:

  • name (str) – user-specified name for solver

  • fixed_factors (dict) – fixed_factors of the solver

See also

base.Solver

check_sample_size()
solve(problem)

Run a single macroreplication of a solver on a problem.

Parameters:

  • problem (Problem object) – simulation-optimization problem to solve

  • crn_across_solns (bool) – indicates if CRN are used when simulating different solutions

Returns:

  • recommended_solns (list of Solution objects) – list of solutions recommended throughout the budget

  • intermediate_budgets (list of ints) – list of intermediate budgets when recommended solutions changes

simopt.solvers.spsa module

Summary

Simultaneous perturbation stochastic approximation (SPSA) is an algorithm for optimizing systems with multiple unknown parameters.

class simopt.solvers.spsa.SPSA(name='SPSA', fixed_factors=None)

Bases: Solver

Simultaneous perturbation stochastic approximation (SPSA) is an algorithm for optimizing systems with multiple unknown parameters.

name

name of solver

Type:

string

objective_type
description of objective types:

“single” or “multi”

Type:

string

constraint_type
description of constraints types:

“unconstrained”, “box”, “deterministic”, “stochastic”

Type:

string

variable_type
description of variable types:

“discrete”, “continuous”, “mixed”

Type:

string

gradient_needed

indicates if gradient of objective function is needed

Type:

bool

factors

changeable factors (i.e., parameters) of the solver

Type:

dict

specifications

details of each factor (for GUI, data validation, and defaults)

Type:

dict

rng_list

list of RNGs used for the solver’s internal purposes

Type:

list of mrg32k3a.mrg32k3a.MRG32k3a objects

Parameters:

  • name (str) – user-specified name for solver

  • fixed_factors (dict) – fixed_factors of the solver

See also

base.Solver

check_alpha()
check_eval_pct()
check_gamma()
check_gavg()
check_iter_pct()
check_n_loss()
check_n_reps()
check_problem_factors()
check_step()
gen_simul_pert_vec(dim)

Generate a new simulatanious pertubation vector with a 50/50 probability discrete distribution, with values of -1 and 1. The vector size is the problem’s dimension. The vector components are independent from each other.

Parameters:

dim (int) – Length of the vector.

Returns:

Vector of -1’s and 1’s.

Return type:

list

solve(problem)

Run a single macroreplication of a solver on a problem.

Parameters:

  • problem (Problem object) – simulation-optimization problem to solve

  • crn_across_solns (bool) – indicates if CRN are used when simulating different solutions

Returns:

  • recommended_solns (list of Solution objects) – list of solutions recommended throughout the budget

  • intermediate_budgets (list of ints) – list of intermediate budgets when recommended solutions changes

simopt.solvers.spsa.check_cons(candidate_x, new_x, lower_bound, upper_bound)

Evaluates the distance from the new vector (candiate_x) compared to the current vector (new_x) respecting the vector’s boundaries of feasibility. Returns the evaluated vector (modified_x) and the weight (t2 - how much of a full step took) of the new vector. The weight (t2) is used to calculate the weigthed average in the ftheta calculation.

simopt.solvers.strong module

Summary

STRONG: A trust-region-based algorithm that fits first- or second-order models through function evaluations taken within a neighborhood of the incumbent solution. A detailed description of the solver can be found here.

class simopt.solvers.strong.STRONG(name='STRONG', fixed_factors=None)

Bases: Solver

A trust-region-based algorithm that fits first- or second-order models through function evaluations taken within a neighborhood of the incumbent solution.

name

name of solver

Type:

string

objective_type
description of objective types:

“single” or “multi”

Type:

string

constraint_type
description of constraints types:

“unconstrained”, “box”, “deterministic”, “stochastic”

Type:

string

variable_type
description of variable types:

“discrete”, “continuous”, “mixed”

Type:

string

gradient_needed

indicates if gradient of objective function is needed

Type:

bool

factors

changeable factors (i.e., parameters) of the solver

Type:

dict

specifications

details of each factor (for GUI, data validation, and defaults)

Type:

dict

rng_list

list of RNGs used for the solver’s internal purposes

Type:

list of mrg32k3a.mrg32k3a.MRG32k3a objects

Parameters:

  • name (str) – user-specified name for solver

  • fixed_factors (dict) – fixed_factors of the solver

See also

base.Solver

cauchy_point(grad, Hessian, new_x, problem)
check_cons(candidate_x, new_x, lower_bound, upper_bound)
check_delta_T()
check_delta_threshold()
check_eta_0()
check_eta_1()
check_gamma_1()
check_gamma_2()
check_lambda()
check_n_r()
check_sensitivity()
finite_diff(new_solution, BdsCheck, stage, problem, n_r)
solve(problem)

Run a single macroreplication of a solver on a problem.

Parameters:

  • problem (Problem object) – simulation-optimization problem to solve

  • crn_across_solns (bool) – indicates if CRN are used when simulating different solutions

Returns:

  • recommended_solns (list of Solution objects) – list of solutions recommended throughout the budget

  • intermediate_budgets (list of ints) – list of intermediate budgets when recommended solutions changes

Module contents