object

conjugate_gradient(Problem)

• Problem - Problem object implementing local_optimization_problem_protocol and defining gradient/2.

Nonlinear conjugate-gradient local optimizer (Fletcher-Reeves and Polak-Ribière). Requires the problem to define gradient/2. Supports optional box constraints via projection, minimization and maximization, and periodic or automatic restarts.

Availability:

logtalk_load(local_optimization(loader))

Author: Paulo Moura

Version: 1:0:0

Date: 2026-09-03

Compilation flags:

static, context_switching_calls

Imports:

public local_optimization_solver(Problem)

Uses:

linear_algebra

list

Remarks:

• Beta formulas: The beta(fletcher_reeves) and beta(polak_ribiere) options select the conjugacy coefficient. Polak-Ribière uses the standard non-negative truncation max(Beta, 0).

• Restarts: The search direction is reset to steepest descent every restart(N) iterations (default: dimension) and whenever the new direction is insufficiently downhill.

• Line search: Backtracking Armijo line search (same parameters as gradient descent).

• Bounds: When the problem defines position_bounds/1, trial points are projected onto the box after each step.

Inherited public predicates:

 check_option/1  check_options/1  default_option/1  default_options/1  option/2  option/3  run/2  run/3  run/4  valid_option/1  valid_options/1

Public predicates

(no local declarations; see entity ancestors if any)

Protected predicates

(no local declarations; see entity ancestors if any)

Private predicates

(no local declarations; see entity ancestors if any)

Operators

(none)

See also

local_optimization_problem_protocol, local_optimization_solver(Problem), bfgs(Problem), gradient_descent(Problem), nelder_mead(Problem)