Solving an Infinite-Horizon Discounted Markov Decision Process by DC Programming and DCA
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Publication:2955913
DOI10.1007/978-3-319-38884-7_4zbMath1358.90148OpenAlexW2460537821MaRDI QIDQ2955913
Publication date: 13 January 2017
Published in: Advanced Computational Methods for Knowledge Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-38884-7_4
Nonconvex programming, global optimization (90C26) Markov and semi-Markov decision processes (90C40)
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Cites Work
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- DC approximation approaches for sparse optimization
- Self-organizing maps by difference of convex functions optimization
- Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms
- Solving a class of linearly constrained indefinite quadratic problems by DC algorithms
- Convex analysis approach to d. c. programming: Theory, algorithms and applications
- The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
- Long-short portfolio optimization under cardinality constraints by difference of convex functions algorithm
- Discrete tomography by convex--concave regularization and D.C. programming
- A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem
- On the Generation of Markov Decision Processes
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