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Submodular maximization generalizes many fundamental problems in discrete optimization, including Max-Cut in directed/undirected graphs, maximum coverage, maximum facility location and marketing over social networks. In this paper we…

Data Structures and Algorithms · Computer Science 2011-01-18 Ariel Kulik , Hadas Shachnai , Tami Tamir

In this paper, a cooperative transmission design for a general multi-node half-duplex wireless relay network is presented. It is assumed that the nodes operate in half-duplex mode and that channel information is available at the nodes. The…

Information Theory · Computer Science 2009-11-17 Debdeep Chatterjee , Tan F. Wong , Tat M. Lok

The goal in thinning is to summarize a dataset using a small set of representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel Halving and Compress can match the quality of uniform subsampling while substantially…

Machine Learning · Statistics 2026-03-03 Annabelle Michael Carrell , Albert Gong , Abhishek Shetty , Raaz Dwivedi , Lester Mackey

Flexible duplex networks allow users to dynamically employ uplink and downlink channels without static time scheduling, thereby utilizing the network resources efficiently. This work investigates the sum-rate maximization of flexible duplex…

Networking and Internet Architecture · Computer Science 2023-03-16 Tharaka Perera , Saman Atapattu , Yuting Fang , Prathapasinghe Dharmawansa , Jamie Evans

We consider the problem of maximizing submodular functions; while this problem is known to be NP-hard, several numerically efficient local search techniques with approximation guarantees are available. In this paper, we propose a novel…

Machine Learning · Computer Science 2013-09-11 K. S. Sesh Kumar , Francis Bach

Approximating the solutions of boundary value problems governed by partial differential equations with neural networks is challenging, largely due to the difficult training process. This difficulty can be partly explained by the spectral…

Numerical Analysis · Mathematics 2025-11-03 Alexander Heinlein , Taniya Kapoor

We study the generalized degrees of freedom (gDoF) of the block-fading noncoherent diamond (parallel relay) wireless network with asymmetric distributions of link strengths, and a coherence time of T symbol duration. We first derive an…

Information Theory · Computer Science 2020-02-20 Joyson Sebastian , Suhas Diggavi

We consider the problem of information flow over Gaussian relay networks. Similar to the recent work by Avestimehr \emph{et al.} [1], we propose network codes that achieve up to a constant gap from the capacity of such networks. However,…

Information Theory · Computer Science 2011-02-28 Theodoros K. Dikaliotis , Hongyi Yao , Salman Avestimehr , Sidharth Jaggi , Tracey Ho

We investigate two new optimization problems -- minimizing a submodular function subject to a submodular lower bound constraint (submodular cover) and maximizing a submodular function subject to a submodular upper bound constraint…

Data Structures and Algorithms · Computer Science 2013-11-12 Rishabh Iyer , Jeff Bilmes

This work focuses on studying the half-duplex (HD) relaying in the Multiple Access Relay Channel (MARC) and the Compound Multiple Access Channel with a Relay (cMACr). A generalized Quantize-and-Forward (GQF) has been proposed to establish…

Information Theory · Computer Science 2014-01-15 Ming Lei , Mohammad Reza Soleymani

Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper…

Information Theory · Computer Science 2021-08-27 Liang Liu , Ya-Feng Liu , Pratik Patil , Wei Yu

Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To…

Signal Processing · Electrical Eng. & Systems 2024-03-18 Xin Hao , Phee Lep Yeoh , Changyang She , Yao Yu , Branka Vucetic , Yonghui Li

Deep learning models are often successfully trained using gradient descent, despite the worst case hardness of the underlying non-convex optimization problem. The key question is then under what conditions can one prove that optimization…

Machine Learning · Computer Science 2017-02-28 Alon Brutzkus , Amir Globerson

Deep Neural Networks have achieved remarkable success relying on the developing high computation capability of GPUs and large-scale datasets with increasing network depth and width in image recognition, object detection and many other…

Machine Learning · Computer Science 2020-01-08 E Zhenqian , Gao Weiguo

In mathematical optimization, second-order Newton's methods generally converge faster than first-order methods, but they require the inverse of the Hessian, hence are computationally expensive. However, we discover that on sparse graphs,…

Machine Learning · Computer Science 2022-05-30 Nima Dehmamy , Csaba Both , Jianzhi Long , Rose Yu

Network robustness is a measure a network's ability to survive adversarial attacks. But not all parts of a network are equal. K-cores, which are dense subgraphs, are known to capture some of the key properties of many real-life networks.…

Social and Information Networks · Computer Science 2020-12-21 Palash Dey , Suman Kalyan Maity , Sourav Medya , Arlei Silva

Node-connectivity augmentation is a fundamental network design problem. We are given a $k$-node connected graph $G$ together with an additional set of links, and the goal is to add a cheap subset of links to $G$ to make it $(k+1)$-node…

Data Structures and Algorithms · Computer Science 2023-11-29 Waldo Galvez , Dylan Hyatt-Denesik , Afrouz Jabal Ameli , Laura Sanita

The diversity-multiplexing tradeoff of the dynamic decode-and-forward protocol is characterized for the half-duplex three-terminal (m,k,n)-relay channel where the source, relay and the destination terminals have m, k and n antennas,…

Information Theory · Computer Science 2010-08-10 Sanjay Karmakar , Mahesh K. Varanasi

Efficiently solving sparse linear systems $Ax=b$, where $A$ is a large, sparse, symmetric positive semi-definite matrix, is a core challenge in scientific computing, machine learning, and optimization. A major bottleneck in Gaussian…

Machine Learning · Computer Science 2025-01-31 Elfarouk Harb , Ho Shan Lam

We consider the problem of dominating set-based virtual backbone used for routing in asymmetric wireless ad-hoc networks. These networks have non-uniform transmission ranges and are modeled using the well-established disk graphs. The…

Networking and Internet Architecture · Computer Science 2015-10-08 Faisal N. Abu-Khzam , Christine Markarian , Friedhelm Meyer auf der Heide , Michael Schubert
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