Name
Title | ||
|---|---|---|
A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees |
Abstract | ||
|---|---|---|
A line search algorithm for minimizing nonconvex and/or nonsmooth objective functions is presented. The algorithm is a hybrid between a standard Broyden–Fletcher–Goldfarb–Shanno (BFGS) and an adaptive gradient sampling (GS) method. The BFGS strategy is employed because it typically yields fast convergence to the vicinity of a stationary point, and together with the adaptive GS strategy the algorithm ensures that convergence will continue to such a point. Under suitable assumptions, it is proved that the algorithm converges globally with probability one. The algorithm has been implemented in C\(++\) and the results of numerical experiments illustrate the efficacy of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/mpc.v0i0.171 | Math. Program. Comput. |
Keywords | Field | DocType |
Nonsmooth optimization, Nonconvex optimization, Unconstrained optimization, Quasi-Newton methods, Gradient sampling, Line search methods, 49M05, 65K05, 65K10, 90C26, 90C30, 90C53, 93B40 | Convergence (routing),Mathematical optimization,Algorithm,Stationary point,Line search,Sampling (statistics),Broyden–Fletcher–Goldfarb–Shanno algorithm,Mathematics | Journal |
Volume | Issue | ISSN |
7 | 4 | 1867-2957 |
Citations | PageRank | References |
4 | 0.39 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank E. Curtis | 1 | 432 | 25.71 |
| Xiaocun Que | 2 | 13 | 0.90 |