Výsledky vyhľadávania - "difference of convex program"

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  1. 1

    Resource Allocation for Multicarrier Rate-Splitting Multiple Access System Autor Li, Lihua, Chai, Kejia, Li, Jilong, Li, Xingwang

    ISSN: 2169-3536, 2169-3536
    Vydavateľské údaje: Piscataway IEEE 2020
    Vydané v IEEE access (2020)
    “…In this article, we investigate the resource allocation problem for the multicarrier rate-splitting multiple access (RSMA) systems. On each subcarrier,…”
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  2. 2

    Consistency bounds and support recovery of d-stationary solutions of sparse sample average approximations Autor Ahn, Miju

    ISSN: 0925-5001, 1573-2916
    Vydavateľské údaje: New York Springer US 01.11.2020
    Vydané v Journal of global optimization (01.11.2020)
    “…This paper studies properties of the d(irectional)-stationary solutions of sparse sample average approximation problems involving difference-of-convex sparsity…”
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  3. 3

    Multipair Two-Way Relay Network With Harvest-Then-Transmit Users: Resolving Pairwise Uplink-Downlink Coupling Autor Wang, Shuai, Xia, Minghua, Wu, Yik-Chung

    ISSN: 1932-4553, 1941-0484
    Vydavateľské údaje: New York IEEE 01.12.2016
    “…While two-way relaying is a promising way to enhance the spectral efficiency of wireless networks, the imbalance of relay-user distances may lead to excessive…”
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  4. 4

    DC approach to weakly convex optimization and nonconvex quadratic optimization problems Autor Nguyen, Linh T. H.

    ISSN: 0233-1934, 1029-4945
    Vydavateľské údaje: Philadelphia Taylor & Francis 01.02.2018
    Vydané v Optimization (01.02.2018)
    “…Motivated by weakly convex optimization and quadratic optimization problems, we first show that there is no duality gap between a difference of convex (DC)…”
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  5. 5

    Confidence Bands for a Log-Concave Density Autor Walther, Guenther, Ali, Alnur, Shen, Xinyue, Boyd, Stephen

    ISSN: 1061-8600, 1537-2715
    Vydavateľské údaje: Alexandria Taylor & Francis 02.10.2022
    “…We present a new approach for inference about a univariate log-concave distribution: Instead of using the method of maximum likelihood, we propose to…”
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