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We consider whether algorithmic choices in over-parameterized linear matrix factorization introduce implicit regularization. We focus on noiseless matrix sensing over rank-$r$ positive semi-definite (PSD) matrices in $\mathbb{R}^{n \times…

Machine Learning · Statistics 2019-09-16 Kelly Geyer , Anastasios Kyrillidis , Amir Kalev

A classical problem in matrix computations is the efficient and reliable approximation of a given matrix by a matrix of lower rank. The truncated singular value decomposition (SVD) is known to provide the best such approximation for any…

Numerical Analysis · Mathematics 2014-08-12 Ming Gu

A primary interest in dynamic inverse problems is to identify the underlying temporal behaviour of the system from outside measurements. In this work we consider the case, where the target can be represented by a decomposition of spatial…

Numerical Analysis · Mathematics 2020-06-09 Simon Arridge , Pascal Fernsel , Andreas Hauptmann

We connect the study of pseudodeterministic algorithms to two major open problems about the structural complexity of $\mathsf{BPTIME}$: proving hierarchy theorems and showing the existence of complete problems. Our main contributions can be…

Computational Complexity · Computer Science 2021-03-16 Zhenjian Lu , Igor C. Oliveira , Rahul Santhanam

The 3-domatic number problem asks whether a given graph can be partitioned intothree dominating sets. We prove that this problem can be solved by a deterministic algorithm in time 2.695^n (up to polynomial factors). This result improves the…

Computational Complexity · Computer Science 2007-05-23 Tobias Riege , Jörg Rothe , Holger Spakowski , Masaki Yamamoto

We study the complexity of testing if two given matroids are isomorphic. The problem is easily seen to be in $\Sigma_2^p$. In the case of linear matroids, which are represented over polynomially growing fields, we note that the problem is…

Computational Complexity · Computer Science 2008-11-25 Raghavendra Rao B. V. , Jayalal M. N. Sarma

We consider the problem of minimizing a linear function over an affine section of the cone of positive semidefinite matrices, with the additional constraint that the feasible matrix has prescribed rank. When the rank constraint is active,…

Systems and Control · Computer Science 2016-11-22 Simone Naldi

Graphical models and factor analysis are well-established tools in multivariate statistics. While these models can be both linked to structures exhibited by covariance and precision matrices, they are generally not jointly leveraged within…

Machine Learning · Statistics 2023-08-02 Alexandre Hippert-Ferrer , Florent Bouchard , Ammar Mian , Titouan Vayer , Arnaud Breloy

Bidimensionality is the most common technique to design subexponential-time parameterized algorithms on special classes of graphs, particularly planar graphs. The core engine behind it is a combinatorial lemma of Robertson, Seymour and…

Data Structures and Algorithms · Computer Science 2019-03-05 Fedor V. Fomin , Daniel Lokshtanov , Fahad Panolan , Saket Saurabh , Meirav Zehavi

This paper describes an algorithm which computes the characteristic polynomial of a matrix over a field within the same asymptotic complexity, up to constant factors, as the multiplication of two square matrices. Previously, this was only…

Symbolic Computation · Computer Science 2021-04-12 Vincent Neiger , Clément Pernet

Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing. This work surveys and extends recent research which…

Numerical Analysis · Mathematics 2014-04-29 Nathan Halko , Per-Gunnar Martinsson , Joel A. Tropp

Most of the existing works on provable guarantees for low-rank matrix completion algorithms rely on some unrealistic assumptions such that matrix entries are sampled randomly or the sampling pattern has a specific structure. In this work,…

Machine Learning · Statistics 2023-06-06 Hanbyul Lee , Rahul Mazumder , Qifan Song , Jean Honorio

We study the problem of finding solutions to the stable matching problem that are robust to errors in the input and we obtain a polynomial time algorithm for a special class of errors. In the process, we also initiate work on a new…

Data Structures and Algorithms · Computer Science 2018-12-17 Tung Mai , Vijay V. Vazirani

The min-rank of a graph was introduced by Haemers (1978) to bound the Shannon capacity of a graph. This parameter of a graph has recently gained much more attention from the research community after the work of Bar-Yossef et al. (2006). In…

Combinatorics · Mathematics 2016-11-26 Son Hoang Dau , Yeow Meng Chee

We consider a matching problem in a bipartite graph $G=(A\cup B,E)$ where nodes in $A$ are agents having preferences in partial order over their neighbors, while nodes in $B$ are objects without preferences. We propose a polynomial-time…

Data Structures and Algorithms · Computer Science 2023-10-05 Telikepalli Kavitha , Tamás Király , Jannik Matuschke , Ildikó Schlotter , Ulrike Schmidt-Kraepelin

We study the problem of bi-chromatic coloring of hypergraphs in the LOCAL distributed model of computation. This problem can easily be solved by a randomized local algorithm with no communication. However, it is not known how to solve it…

Data Structures and Algorithms · Computer Science 2019-08-01 Dariusz R. Kowalski , Piotr Krysta

This paper is centered on the complexity of graph problems in the well-studied LOCAL model of distributed computing, introduced by Linial [FOCS '87]. It is widely known that for many of the classic distributed graph problems (including…

Data Structures and Algorithms · Computer Science 2017-10-31 Mohsen Ghaffari , Fabian Kuhn , Yannic Maus

In this paper, we consider the problem of computing the rank of a block-structured symbolic matrix (a generic partitioned matrix) $A = (A_{\alpha \beta} x_{\alpha \beta})$, where $A_{\alpha \beta}$ is a $2 \times 2$ matrix over a field…

Combinatorics · Mathematics 2021-06-23 Hiroshi Hirai , Yuni Iwamasa

We show that any submodular minimization (SM) problem defined on a linear constraint set with constraints having up to two variables per inequality, are 2-approximable in polynomial time. If the constraints are monotone (the two variables…

Discrete Mathematics · Computer Science 2017-05-01 Dorit S. Hochbaum

An old idea in optimization theory says that since the gradient is a dual vector it may not be subtracted from the weights without first being mapped to the primal space where the weights reside. We take this idea seriously in this paper…

Machine Learning · Computer Science 2024-12-09 Jeremy Bernstein , Laker Newhouse