L-BFGS-B
A limited-memory quasi-Newton optimization algorithm that supports bound constraints. It approximates the Hessian matrix using gradient and displacement information from a recent number of steps and is suited to medium- and high-dimensional continuous optimization problems.
Timeline (last 90 days)
The study compares five budget-matched continuous optimizers (including a hybrid swarm optimization with L-BFGS-B refinement) against the candidate-pool method, covering 11 problems (dimensions from 2 to 100).
L-BFGS-B is a quasi-Newton method belonging to the limited-memory variant of the Broyden–Fletcher–Goldfarb–Shanno algorithm, and it supports imposing bound constraints on variables.
L-BFGS-B uses gradient and displacement information from the most recent several steps to approximate the Hessian matrix, requiring no storage of the full Hessian, and is suitable for medium- to high-dimensional continuous optimization problems.
All Facts (3)
The study compares five budget-matched continuous optimizers (including a hybrid swarm optimization with L-BFGS-B refinement) against the candidate-pool method, covering 11 problems (dimensions from 2 to 100).
50%UnverifiedL-BFGS-B is a quasi-Newton method belonging to the limited-memory variant of the Broyden–Fletcher–Goldfarb–Shanno algorithm, and it supports imposing bound constraints on variables.
50%UnverifiedL-BFGS-B uses gradient and displacement information from the most recent several steps to approximate the Hessian matrix, requiring no storage of the full Hessian, and is suitable for medium- to high-dimensional continuous optimization problems.
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