linesearch

Line-search-based deterministic optimization methods.

This module implements gradient-based algorithms that share a common line search interface, including gradient descent, BFGS, and Newton-CG.

LineSearch

Bases: OptimizerBase

Line-search optimizer compatible with OptimizerBase.

The class supports gradient descent, BFGS, and Newton-CG search directions, together with either Wolfe or backtracking line search. It can operate with bounds, optional state transformations, logging, result persistence, and restart checkpoints.

__init__(x0, fun, method='GD', jac=None, hess=None, args=(), bounds=None, callback=None, **options)

Initialize a line-search optimizer instance.

Parameters:

Name	Type	Description	Default
x0	ndarray	Initial parameter vector.	required
fun	callable	Objective function.	required
method	(GD, BFGS, Newton - CG)	Search-direction method.	'GD'
jac	callable	Gradient function.	None
hess	callable	Hessian function, required by Newton-CG.	None
args	tuple	Extra positional arguments passed to the wrapped callables.	()
bounds	sequence	Lower and upper bounds for each state variable.	None
callback	callable	Callback invoked after successful updates.	None
**options		Line-search configuration, plus everything :class:OptimizerBase takes. - step_size: Initial step size (default: None, auto-scaled). - step_size_max: Maximum step size (default: 1e5). - step_size_adapt: Step size adaptation strategy (0: none, 1: function-based, 2: gradient-based). Default is 1 (function-based). - c1: Armijo condition constant (default: 1e-4). - c2: Curvature condition constant (default: 0.9). - rho: Step size reduction factor for backtracking (default: 0.5). - lsmaxiter: Maximum line search iterations (default: 10). - lsmethod: Line search method (0: backtracking, 1: Wolfe, default: 1). - normalize: Whether to normalize the search direction (default: False). - recompute_jac: Number of gradient recomputation attempts on line search failure (default: 0). - hess0_inv: Initial inverse-Hessian approximation for BFGS (default: identity).	{}

log_columns()

The row of the iteration log: iteration, objective, gradient infinity norm, step length taken.

update_step()

Perform one optimization step.

The method computes a search direction, performs a line search, and commits the new iterate on success. When enabled, it can recompute the gradient and retry if the line search fails.