Title | ||
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Block Alternating Optimization For Non-Convex Min-Max Problems: Algorithms And Applications In Signal Processing And Communications |
Abstract | ||
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The min-max problem, also known as the saddle point problem, can be used to formulate a wide range of applications in signal processing and wireless communications. However, existing optimization theory and methods, which mostly deal with problems with certain convex-concave structure, are not applicable for the aforementioned applications, which oftentimes involve non-convexity. In this work, we consider a general block-wise one-sided non-convex min-max problem, in which the minimization problem consists of multiple blocks and is non-convex, while the maximization problem is (strongly) concave. We propose two simple algorithms, which alternatingly perform one gradient descent-type step for each minimization block and one gradient ascent-type step for the maximization problem. For the first time, we show that such simple alternating min-max algorithms converge to first-order stationary solutions. We conduct numerical tests on a robust learning problem, and a wireless communication problem in the presence of jammers, to validate the efficiency of the proposed algorithms. |
Year | DOI | Venue |
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2019 | 10.1109/icassp.2019.8683795 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Field | DocType | ISSN |
Signal processing,Numerical tests,Mathematical optimization,Saddle point,Wireless,Computer science,Algorithm,Regular polygon,Minification,SIMPLE algorithm,Maximization | Conference | 1520-6149 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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Songtao Lu | 1 | 84 | 19.52 |
Ioannis Tsaknakis | 2 | 1 | 1.02 |
Mingyi Hong | 3 | 1533 | 91.29 |