Limited-Memory BFGS with Displacement Aggregation
Autor: | Albert S. Berahas, Frank E. Curtis, Baoyu Zhou |
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Rok vydání: | 2019 |
Předmět: |
Hessian matrix
021103 operations research General Mathematics Numerical analysis 0211 other engineering and technologies MathematicsofComputing_NUMERICALANALYSIS Inverse 010103 numerical & computational mathematics 02 engineering and technology Curvature 01 natural sciences Displacement (vector) symbols.namesake Rate of convergence Optimization and Control (math.OC) Broyden–Fletcher–Goldfarb–Shanno algorithm Convergence (routing) symbols FOS: Mathematics Applied mathematics 0101 mathematics Mathematics - Optimization and Control Software Mathematics |
DOI: | 10.48550/arxiv.1903.03471 |
Popis: | A displacement aggregation strategy is proposed for the curvature pairs stored in a limited-memory BFGS (a.k.a. L-BFGS) method such that the resulting (inverse) Hessian approximations are equal to those that would be derived from a full-memory BFGS method. This means that, if a sufficiently large number of pairs are stored, then an optimization algorithm employing the limited-memory method can achieve the same theoretical convergence properties as when full-memory (inverse) Hessian approximations are stored and employed, such as a local superlinear rate of convergence under assumptions that are common for attaining such guarantees. To the best of our knowledge, this is the first work in which a local superlinear convergence rate guarantee is offered by a quasi-Newton scheme that does not either store all curvature pairs throughout the entire run of the optimization algorithm or store an explicit (inverse) Hessian approximation. Numerical results are presented to show that displacement aggregation within an adaptive L-BFGS scheme can lead to better performance than standard L-BFGS. |
Databáze: | OpenAIRE |
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