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The problem of determining the best achievable performance of arbitrary lossless compression algorithms is examined, when correlated side information is available at both the encoder and decoder. For arbitrary source-side information pairs,…

Information Theory · Computer Science 2020-07-15 Lampros Gavalakis , Ioannis Kontoyiannis

We consider a family of parallel methods for constrained optimization based on projected gradient descents along individual coordinate directions. In the case of polyhedral feasible sets, local convergence towards a regular solution occurs…

Optimization and Control · Mathematics 2015-09-18 Olivier Bilenne

We determine the exact error and strong converse exponents of shared randomness-assisted channel simulation in worst case total-variation distance. Namely, we find that these exponents can be written as simple optimizations over the R\'enyi…

Information Theory · Computer Science 2024-10-10 Aadil Oufkir , Michael X. Cao , Hao-Chung Cheng , Mario Berta

We introduce the class of partition-balanced families of codes, and show how to exploit their combinatorial invariants to obtain upper and lower bounds on the number of codes that have a prescribed property. In particular, we derive precise…

Information Theory · Computer Science 2018-12-13 Eimear Byrne , Alberto Ravagnani

This work establishes the exact exponents for the soft-covering phenomenon of a memoryless channel under the total variation metric when random (i.i.d. and constant-composition) channel codes are used. The exponents, established herein, are…

Information Theory · Computer Science 2019-06-26 Semih Yagli , Paul Cuff

We prove a general quantitative theorem on the asymptotic behavior of stochastic quasi-Fej\'er monotone sequences in a broad metric context. Concretely, our result explicitly constructs a rate of convergence for such process, both in mean…

Optimization and Control · Mathematics 2026-05-08 Nicholas Pischke , Thomas Powell

We consider linear two-time-scale stochastic approximation algorithms driven by martingale noise. Recent applications in machine learning motivate the need to understand finite-time error rates, but conventional stochastic approximation…

Machine Learning · Computer Science 2025-12-12 Seo Taek Kong , Sihan Zeng , Thinh T. Doan , R. Srikant

We consider a multi-agent network where each node has a stochastic (local) cost function that depends on the decision variable of that node and a random variable, and further the decision variables of neighboring nodes are pairwise…

Optimization and Control · Mathematics 2021-12-24 Navjot Singh , Xuanyu Cao , Suhas Diggavi , Tamer Basar

In this paper, we study the asymptotic performance of Abelian group codes for the lossy source coding problem for arbitrary discrete (finite alphabet) memoryless sources as well as the channel coding problem for arbitrary discrete (finite…

Information Theory · Computer Science 2013-05-08 Aria G. Sahebi , S. Sandeep Pradhan

Previous works show convergence of rational Chebyshev approximants to the Pad\'e approximant as the underlying domain of approximation shrinks to the origin. In the present work, the asymptotic error and interpolation properties of rational…

Numerical Analysis · Mathematics 2024-10-08 Tobias Jawecki

We establish the optimal nonergodic sublinear convergence rate of the proximal point algorithm for maximal monotone inclusion problems. First, the optimal bound is formulated by the performance estimation framework, resulting in an infinite…

Optimization and Control · Mathematics 2019-07-15 Guoyong Gu , Junfeng Yang

We investigate the maximum coding rate achievable on a two-user broadcast channel for the case where a common message is transmitted with feedback using either fixed-blocklength codes or variable-length codes. For the fixed-blocklength-code…

Information Theory · Computer Science 2018-09-03 Kasper Fløe Trillingsgaard , Wei Yang , Giuseppe Durisi , Petar Popovski

Randomized subspace approximation with "matrix sketching" is an effective approach for constructing approximate partial singular value decompositions (SVDs) of large matrices. The performance of such techniques has been extensively…

Numerical Analysis · Mathematics 2024-06-28 Yijun Dong , Per-Gunnar Martinsson , Yuji Nakatsukasa

We consider the maximum coding rate achievable by uniformly-random codes for the deletion channel. We prove an upper bound that's within 0.1 of the best known lower bounds for all values of the deletion probability $d,$ and much closer for…

Information Theory · Computer Science 2022-10-17 Berivan Isik , Francisco Pernice , Tsachy Weissman

Polyak-Ruppert averaging is a widely used technique to achieve the optimal asymptotic variance of stochastic approximation (SA) algorithms, yet its high-probability performance guarantees remain underexplored in general settings. In this…

Machine Learning · Statistics 2025-05-29 Sajad Khodadadian , Martin Zubeldia

In this work, we study the performance of random isometric precoders over quasi-static and correlated fading channels. We derive deterministic approximations of the mutual information and the signal-to-interference-plus-noise ratio (SINR)…

Information Theory · Computer Science 2016-11-18 Romain Couillet , Jakob Hoydis , Merouane Debbah

A new single-letter achievable rate region is proposed for the two-user discrete memoryless multiple-access channel(MAC) with noiseless feedback. The proposed region includes the Cover-Leung rate region [1], and it is shown that the…

Information Theory · Computer Science 2014-03-31 Ramji Venkataramanan , S. Sandeep Pradhan

In this work, we consider a discrete-time stationary Rayleigh flat-fading channel with unknown channel state information at transmitter and receiver. The law of the channel is presumed to be known to the receiver. In addition, we assume the…

Information Theory · Computer Science 2011-03-03 Meik Dörpinghaus , Heinrich Meyr

This paper provides upper and lower bounds on the optimal guessing moments of a random variable taking values on a finite set when side information may be available. These moments quantify the number of guesses required for correctly…

Information Theory · Computer Science 2018-06-22 Igal Sason , Sergio Verdú

Weak superimposed codes are combinatorial structures related closely to generalized cover-free families, superimposed codes, and disjunct matrices in that they are only required to satisfy similar but less stringent conditions. This class…

Information Theory · Computer Science 2024-09-17 Yu Tsunoda , Yuichiro Fujiwara