Package-level declarations
Types
An interface to define the sampling mechanism used within the Cross-Entropy method. Implementors of this interface are responsible for generating samples from the cross-entropy "distribution", updating the parameters based on observations of the elite samples, and determining if the sampling distribution has degenerated (converged).
Abstract base class for defining cooling schedules in optimization algorithms like simulated annealing. A cooling schedule manages the temperature reduction process as the algorithm iterates.
Defines the interface for a cooling schedule used in optimization algorithms such as simulated annealing. A cooling schedule is responsible for managing the temperature reduction process as the algorithm iterates.
Constructs an instance of CrossEntropySolver with specified parameters.
If supplied, this function will be used to determine the size of the elite sample during the cross-entropy process. Supplying a function can permit dynamic changes when determining the elite sample.
Represents a cooling schedule where the temperature decreases exponentially at each iteration according to a specified cooling rate.
Represents a linear cooling schedule used in optimization algorithms like simulated annealing. The temperature decreases linearly with each iteration based on the specified parameters.
Implements a logarithmic cooling schedule for optimization algorithms such as simulated annealing. The temperature decreases proportionally to the inverse of the natural logarithm of the iteration number.
If supplied, this function will be used to determine the size of the cross-entropy sample during the cross-entropy process. Supplying a function can permit dynamic changes when determining the size of the cross-entropy sample (population).
A class that implements the simulated annealing optimization algorithm for solving stochastic or deterministic problems. Simulated annealing is an iterative optimization method inspired by the physical process of annealing in metallurgy. It uses a probabilistic approach to escape local optima by accepting worse solutions with a certain probability, which decreases over time according to a cooling schedule.
A class that implements the Stochastic Hill Climbing optimization algorithm. This algorithm searches for an optimal solution by iteratively evaluating and moving to neighborhood solutions, as determined by the evaluator and the problem definition. It uses a stochastic approach to explore the solution space by leveraging a random number stream.
Represents an abstract base class for stochastic solvers. This class provides foundational functionality for solvers that utilize randomness during their optimization process.