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A proximal method for composite minimization
Open accessAbstract
We consider minimization of functions that are compositions of convex or prox-regular functions (possibly extended-valued) with smooth vector functions. A wide variety of important optimization problems fall into this framework. We describe an algorithmic framework based on a subproblem constructed from a linearized approximation to the objective and a regularization term. Properties of local solutions of this subproblem underlie both a global convergence result and an identification property of the active manifold containing the solution of the original problem. Preliminary computational results on both convex and nonconvex examples are promising.
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Cited by
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Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., & Ma, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.112 · Cited by 629
An optimal method for stochastic composite optimization
Lan, G. (2012) · Mathematical Programming · doi:10.1007/s10107-010-0434-y · Cited by 427
Ghadimi, S., & Lan, G. (2012) · SIAM Journal on Optimization · doi:10.1137/110848864 · Cited by 269
Stochastic Model-Based Minimization of Weakly Convex Functions
Davis, D., & Drusvyatskiy, D. (2019) · SIAM Journal on Optimization · doi:10.1137/18m1178244 · Cited by 258
Metadata
- Authors
- Lewis, A. S., & Wright, S. J.
- Venue
- Mathematical Programming
- Published
- 2015-08-29
- Volume
- 158
- Issue
- 1-2
- Pages
- 501-546
- ISSN
- 0025-5610 (ISSN-L)
- 1436-4646
- OpenAlex
- W1677069601
- Cited by
- 167
- References
- 0
- FWCI
- 10.98
- Type
- journal article
- Language
- en
- Open access
- green
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