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Structure-aware methods for expensive derivative-free nonsmooth composite optimization
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DOI：10.1007/s12532-023-00245-5.png)
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摘要
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We present new methods for solving a broad class of bound-constrained nonsmooth composite minimization problems. These methods are specially designed for objectives that are some known mapping of outputs from a computationally expensive function. We provide accompanying implementations of these methods: in particular, a novel manifold sampling algorithm (MS-P) with subproblems that are in a sense primal versions of the dual problems solved by previous manifold sampling methods and a method (GOOMBAH) that employs more difficult optimization subproblems. For these two methods, we provide rigorous convergence analysis and guarantees. We demonstrate extensive testing of these methods. Open-source implementations of the methods developed in this manuscript can be found at https://github.com/POptUS/ IBCDFO/.
Keyword:
Derivative-free optimization
Nonsmooth optimization
Composite optimization
Continuous selections
Manifold sampling
期刊
IF:
3.6
论文数:
201
被引数:
1.9K
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