simopt.solvers.neldmd

Nelder-Mead Algorithm.

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.

Module Contents

class simopt.solvers.neldmd.NelderMeadConfig

Bases: simopt.base.SolverConfig

Configuration for Nelder-Mead solver.

r: Annotated[int, Field(default=30, gt=0, description='number of replications taken at each solution')]

alpha: Annotated[float, Field(default=1.0, gt=0, description='reflection coefficient > 0')]

gammap: Annotated[float, Field(default=2.0, gt=1, description='expansion coefficient > 1')]

betap: Annotated[float, Field(default=0.5, gt=0, lt=1, description='contraction coefficient > 0, < 1')]

delta: Annotated[float, Field(default=0.5, gt=0, lt=1, description='shrink factor > 0, < 1')]

sensitivity: Annotated[float, Field(default=1e-07, gt=0, description='shrinking scale for bounds')]

initial_spread: Annotated[float, Field(default=0.1, gt=0, description='fraction of distance between bounds used for initial points')]

class simopt.solvers.neldmd.NelderMead(name: str = '', fixed_factors: dict | None = None)

Bases: simopt.base.Solver

Nelder-Mead Algorithm.

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.

Initialize a solver object.

Parameters:

• name (str, optional) – Name of the solver. Defaults to an empty string.

• fixed_factors (dict | None, optional) – Dictionary of user-specified solver factors. Defaults to None.

name: str = 'NELDMD'

config_class: ClassVar[type[simopt.base.SolverConfig]]

Configuration class for the solver.

class_name_abbr: ClassVar[str] = 'NELDMD'

Short name of the solver class.

class_name: ClassVar[str] = 'Nelder-Mead'

Long name of the solver class.

objective_type: ClassVar[simopt.base.ObjectiveType]

Description of objective types.

constraint_type: ClassVar[simopt.base.ConstraintType]

Description of constraint types.

variable_type: ClassVar[simopt.base.VariableType]

Description of variable types.

gradient_needed: ClassVar[bool] = False

True if gradient of objective function is needed, otherwise False.

solve(problem: simopt.base.Problem) → None

Run a single macroreplication of a solver on a problem.

Parameters:

problem (Problem) – Simulation-optimization problem to solve.

Returns:

• list [Solution]: List of solutions recommended throughout the budget.

• list [int]: List of intermediate budgets when recommended solutions

  change.

Return type:

tuple