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We study implicit regularization when optimizing an underdetermined quadratic objective over a matrix $X$ with gradient descent on a factorization of $X$. We conjecture and provide empirical and theoretical evidence that with small enough…

Machine Learning · Statistics 2017-05-26 Suriya Gunasekar , Blake Woodworth , Srinadh Bhojanapalli , Behnam Neyshabur , Nathan Srebro

We propose a unified framework to solve general low-rank plus sparse matrix recovery problems based on matrix factorization, which covers a broad family of objective functions satisfying the restricted strong convexity and smoothness…

Machine Learning · Statistics 2018-02-21 Xiao Zhang , Lingxiao Wang , Quanquan Gu

We propose \textit{Meta-Regularization}, a novel approach for the adaptive choice of the learning rate in first-order gradient descent methods. Our approach modifies the objective function by adding a regularization term on the learning…

Machine Learning · Computer Science 2021-04-13 Guangzeng Xie , Hao Jin , Dachao Lin , Zhihua Zhang

We present a method to design parallel algorithms for constrained combinatorial optimization problems. Our method solves and generalizes many classical combinatorial optimization problems including the stable marriage problem, the shortest…

Data Structures and Algorithms · Computer Science 2018-12-27 Vijay K. Garg

A lattice of integers is the collection of all linear combinations of a set of vectors for which all entries of the vectors are integers and all coefficients in the linear combinations are also integers. Lattice reduction refers to the…

Cryptography and Security · Computer Science 2024-04-09 François Charton , Kristin Lauter , Cathy Li , Mark Tygert

In this paper, we give a way to construct graded filtrations of graded modules. We then apply it to the Sally module, which describes a correction term of the Hilbert function. As a result, we obtain the inequality of the Hilbert…

Commutative Algebra · Mathematics 2021-10-11 Shinya Kumashiro

In this paper, we propose a new detection technique for multiuser multiple-input multiple-output (MU-MIMO) systems. The proposed scheme combines a lattice reduction (LR) transformation, which makes the channel matrix nearly orthogonal, and…

Information Theory · Computer Science 2014-12-09 L. Arevalo , R. C. de Lamare , R. Sampaio-Neto

Can the behavior of a random matrix be improved by modifying a small fraction of its entries? Consider a random matrix $A$ with i.i.d. entries. We show that the operator norm of $A$ can be reduced to the optimal order $O(\sqrt{n})$ by…

Probability · Mathematics 2017-11-02 Elizaveta Rebrova , Roman Vershynin

In this paper, we propose the first continuous optimization algorithms that achieve a constant factor approximation guarantee for the problem of monotone continuous submodular maximization subject to a linear constraint. We first prove that…

Data Structures and Algorithms · Computer Science 2020-06-23 Moran Feldman , Amin Karbasi

We analyze a new random algorithm for numerical integration of $d$-variate functions over $[0,1]^d$ from a weighted Sobolev space with dominating mixed smoothness $\alpha\ge 0$ and product weights $1\ge\gamma_1\ge\gamma_2\ge\cdots>0$, where…

Numerical Analysis · Mathematics 2019-08-15 Peter Kritzer , Frances Y. Kuo , Dirk Nuyens , Mario Ullrich

Trace norm regularization is a widely used approach for learning low rank matrices. A standard optimization strategy is based on formulating the problem as one of low rank matrix factorization which, however, leads to a non-convex problem.…

Machine Learning · Computer Science 2017-08-01 Carlo Ciliberto , Dimitris Stamos , Massimiliano Pontil

The Korkine--Zolotareff (KZ) reduction, and its generalisations, are widely used lattice reduction strategies in communications and cryptography. The KZ constant and Schnorr's constant were defined by Schnorr in 1987. The KZ constant can be…

Information Theory · Computer Science 2022-04-19 Jinming Wen , Xiao-Wen Chang

We consider the problem of recovering a low-rank matrix from its clipped observations. Clipping is conceivable in many scientific areas that obstructs statistical analyses. On the other hand, matrix completion (MC) methods can recover a…

Machine Learning · Computer Science 2019-03-05 Takeshi Teshima , Miao Xu , Issei Sato , Masashi Sugiyama

We develop a unifying framework for interpolatory $\mathcal{L}_2$-optimal reduced-order modeling for a wide classes of problems ranging from stationary models to parametric dynamical systems. We first show that the framework naturally…

Numerical Analysis · Mathematics 2023-09-26 Petar Mlinarić , Serkan Gugercin

A Bounded-Degree Low-Rank Parity-Check (BD-LRPC) code is a rank-metric code that admits a parity-check matrix whose support is generated by a set of powers of an element. This specific structure of the parity-check matrix was employed to…

Information Theory · Computer Science 2025-09-17 Hermann Tchatchiem Kamche

In multiple-input multiple-output (MIMO) fading channels maximum likelihood (ML) detection is desirable to achieve high performance, but its complexity grows exponentially with the spectral efficiency. The current state of the art in MIMO…

Information Theory · Computer Science 2007-07-13 Massimiliano Siti , Michael P. Fitz

Computing Fourier transforms of k-sparse signals, where only k of N frequencies are non-zero, is fundamental in compressed sensing, radar, and medical imaging. While the Fast Fourier Transform (FFT) evaluates all N frequencies in $O(N \log…

Signal Processing · Electrical Eng. & Systems 2026-04-22 Aaron R. Flouro , Shawn P. Chadwick

We study a submodular maximization problem motivated by applications in online retail. A platform displays a list of products to a user in response to a search query. The user inspects the first $k$ items in the list for a $k$ chosen at…

Computer Science and Game Theory · Computer Science 2022-10-24 Arash Asadpour , Rad Niazadeh , Amin Saberi , Ali Shameli

We design two Recoverable Mutual Exclusion (RME) locks for the system-wide crash model. Our first algorithm requires only $O(1)$ space per process, and achieves $O(1)$ worst-case RMR complexity in the CC model. Our second algorithm enhances…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-02-03 Prasad Jayanti , Siddhartha Jayanti , Anup Joshi

In this work we apply the Dispersive Matrix (DM) method of Refs. [1,2] to the lattice computations of the Form Factors (FFs) entering the semileptonic $B \to D^* \ell \nu_\ell$ decays, recently produced by the FNAL/MILC Collaborations [3]…

High Energy Physics - Phenomenology · Physics 2022-12-21 G. Martinelli , S. Simula , L. Vittorio