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- Title
Self-adaptive algorithms for quasiconvex programming and applications to machine learning.
- Authors
Thang, Tran Ngoc; Hai, Trinh Ngoc
- Abstract
For solving a broad class of nonconvex programming problems on an unbounded constraint set, we provide a self-adaptive step-size strategy that does not include line-search techniques and establishes the convergence of a generic approach under mild assumptions. Specifically, the objective function may not satisfy the convexity condition. Unlike descent line-search algorithms, it does not need a known Lipschitz constant to figure out how big the first step should be. The crucial feature of this process is the steady reduction of the step size until a certain condition is fulfilled. In particular, it can provide a new gradient projection approach to optimization problems with an unbounded constrained set. To demonstrate the effectiveness of the proposed technique for large-scale problems, we apply it to some experiments on machine learning, such as supervised feature selection, multi-variable logistic regressions and neural networks for classification.
- Subjects
MACHINE learning; NONCONVEX programming; ALGORITHMS; FEATURE selection; SELF-adaptive software; CONSTRAINT programming; LOGISTIC regression analysis
- Publication
Computational & Applied Mathematics, 2024, Vol 43, Issue 4, p1
- ISSN
0101-8205
- Publication type
Article
- DOI
10.1007/s40314-024-02764-w