Search Results - Projected sub-gradient algorithm~

Efficient spectrum scheduling and power management for opportunistic users by Masmoudi, Raouia, Belmega, E. Veronica, Fijalkow, Inbar

“…In this paper, we study the centralized spectrum access and power management for several opportunistic users, secondary users (SUs), without hurting the…”
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Distributed Unbalanced Optimization Design Over Nonidentical Constraints by Huang, Qing, Fan, Yuan, Cheng, Songsong

“… To solve the problem, we introduce the distributed projected sub-gradient algorithm with a row-stochastic weight matrix over unbalanced digraphs…”
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Efficient Design of Multi-group Multicast Beamforming via Reconfigurable Intelligent Surface by Ebrahimi, Mohammad, Dong, Min

“…). We propose a fast and scalable algorithm for the joint design of the base station (BS) multicast beamforming and the RIS passive beamforming to minimize the transmit power subject to the quality-of-service (QoS) constraints…”
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Projection-Free Non-Smooth Convex Programming by Asgari, Kamiar, Neely, Michael J

“… Thus, the proposed algorithm is a projection-free alternative to the Projected sub-Gradient Descent (PGD…”
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Distributed Nonsmooth Optimization With Coupled Inequality Constraints via Modified Lagrangian Function by Liang, Shu, Zeng, Xianlin, Hong, Yiguang

“… Then, we construct a distributed continuous-time algorithm by virtue of a projected primal-dual subgradient dynamics…”
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Policy-based Primal-Dual Methods for Concave CMDP with Variance Reduction by Ying, Donghao, Guo, Mengzi Amy, Lee, Hyunin, Ding, Yuhao, Lavaei, Javad, Shen, Zuo-Jun Max

“… We propose the Variance-Reduced Primal-Dual Policy Gradient Algorithm (VR-PDPG), which updates the primal variable via policy gradient ascent and the dual variable via projected sub-gradient descent…”
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New Projected Gradient Method to Solve Variational Inequality Problem by Shan, Zachary

“… We introduce a generalized sub gradient projection operator that expands the search range of each iteration of the algorithm…”
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Projected sub-gradient with ℓ1 or simplex constraints via isotonic regression by Thai, Jerome, Wu, Cathy, Pozdnukhov, Alexey, Bayen, Alexandre

“… methods which compare well against projected algorithms using direct…”
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Scalable algorithms for locally low-rank matrix modeling by Gu, Qilong, Trzasko, Joshua D., Banerjee, Arindam

“… In this paper, we consider a convex relaxation of LLR structure and propose an efficient algorithm based on dual projected gradient descent (D-PGD…”
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Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information by Kasparick, Martin, Cavalcante, Renato L. G., Valentin, Stefan, Stanczak, Slawomir, Yukawa, Masahiro

“… The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method (APSM…”
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Exploiting Locality and Structure for Distributed Optimization in Multi-Agent Systems by Brown, Robin, Rossi, Federico, Solovey, Kiril, Wolf, Michael T., Pavone, Marco

“… Nevertheless, existing algorithms for distributed optimization generally do not exploit the locality structure of the problem, requiring all agents to compute or exchange the full set of decision variables…”
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Computationally Efficient and Statistically Optimal Robust High-Dimensional Linear Regression by Shen, Yinan, Li, Jingyang, Jian-Feng, Cai, Xia, Dong

“… In this paper, we introduce a projected sub-gradient descent algorithm for both the sparse linear regression and low-rank linear regression problems…”
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An Improved Linear Discriminant Analysis with L1-Norm for Robust Feature Extraction by Xiaobo Chen, Jian Yang, Zhong Jin

“… To address this issue, we develop a novel algorithm termed as ILDA-L1 in this paper, which can optimize all the discriminant vectors simultaneously in a unified framework…”
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Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing by Matsumoto, Namiko, Mazumdar, Arya

“…Compressed sensing has been a very successful high-dimensional signal acquisition and recovery technique that relies on linear operations. However, the actual…”
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Distributed Robust Optimization in Networked System by Wang, Shengnan, Li, Chunguang

“…In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex…”
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Quantile and pseudo-Huber Tensor Decomposition by Shen, Yinan, Xia, Dong

“… We propose a projected sub-gradient descent algorithm for tensor decomposition, equipped with either the pseudo-Huber loss or the quantile loss…”
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Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension by Giampouras, Paris V, Haeffele, Benjamin D, Vidal, René

“…Robust subspace recovery (RSR) is a fundamental problem in robust representation learning. Here we focus on a recently proposed RSR method termed Dual…”
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Proximal-Gen for fast compressed sensing recovery by Cai, Lei, Fu, Yuli, Zhu, Tao, Xiang, Youjun, Zeng, Huanqiang

“… Then based on the general domain, we develop a fast recovery algorithm, which mainly consists of two sub-algorithms, namely network-based projected gradient descent…”
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Robustness of DC Power Networks under Weight Control by Ba, Qin, Savla, Ketan

“…We study, possibly distributed, robust weight control policies for DC power networks that change link susceptances, or weights in response to balanced…”
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Scalable Algorithms for Locally Low-Rank Matrix Modeling by Qilong Gu, Trzasko, Joshua D., Banerjee, Arindam

“… In this paper, we consider a convex relaxation of LLR structure, and propose an efficient algorithm based on dual projected gradient descent (D-PGD…”
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