A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees
- 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 inC++and the results of numerical experiments illustrate the efficacy of the proposed approach.
Metadaten| Author: | Frank E. Curtis, Xiaocun Que |
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| DOI: | https://doi.org/10.1007/s12532-015-0086-2 |
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| ISSN: | 1867-2949 |
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| Parent Title (English): | Mathematical Programming Computation |
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| Publisher: | Springer Science and Business Media LLC |
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| Document Type: | Article |
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| Language: | English |
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| Year of Completion: | 2015 |
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| Tag: | Software; Theoretical Computer Science |
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| Volume: | 7 |
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| Issue: | 4 |
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| Page Number: | 30 |
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| First Page: | 399 |
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| Last Page: | 428 |
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| Mathematical Programming Computation : | MPC 2015 - Issue 4 |
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