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The linear complementarity problem (LCP) provides a unified approach to many problems such as linear programs, convex quadratic programs, and bimatrix games. The general LCP is known to be NP-hard, but there are some promising results that…

Combinatorics · Mathematics 2014-07-14 Lorenz Klaus , Hiroyuki Miyata

Compressive sensing (CS) has triggered enormous research activity since its first appearance. CS exploits the signal's sparsity or compressibility in a particular domain and integrates data compression and acquisition, thus allowing exact…

Computer Vision and Pattern Recognition · Computer Science 2015-06-16 Shmuel Friedland , Qun Li , Dan Schonfeld

We study global solutions to the thin obstacle problem with at most quadratic growth at infinity. We show that every ellipsoid can be realized as the contact set of such a solution. On the other hand, if such a solution has a compact…

Analysis of PDEs · Mathematics 2024-04-02 Simon Eberle , Hui Yu

In this thesis, we present results related to complementarity problems. We study the linear complementarity problems on extended second order cones. We convert a linear complementarity problem on an extended second order cone into a mixed…

Optimization and Control · Mathematics 2021-08-18 Lianghai Xiao

The Promise Constraint Satisfaction Problem (PCSP for short) is a generalization of the well-studied Constraint Satisfaction Problem (CSP). The PCSP has its roots in such classic problems as the Approximate Graph Coloring and the…

Computational Complexity · Computer Science 2025-12-08 Arash Beikmohammadi , Andrei A. Bulatov

Many isomorphism problems for tensors, groups, algebras, and polynomials were recently shown to be equivalent to one another under polynomial-time reductions, prompting the introduction of the complexity class TI (Grochow & Qiao, ITCS '21;…

Computational Complexity · Computer Science 2024-04-15 Joshua A. Grochow , Youming Qiao

In [13], Hillar and Lim famously demonstrated that "multilinear (tensor) analogues of many efficiently computable problems in numerical linear algebra are NP-hard". Despite many recent advancements, the state-of-the-art methods for…

Optimization and Control · Mathematics 2016-11-08 Bo Jiang , Fan Yang , Shuzhong Zhang

Tensor decomposition is a well-known tool for multiway data analysis. This work proposes using stochastic gradients for efficient generalized canonical polyadic (GCP) tensor decomposition of large-scale tensors. GCP tensor decomposition is…

Numerical Analysis · Mathematics 2020-11-25 Tamara G. Kolda , David Hong

Combinatorial discrepancy is a complexity measure of a collection of sets which quantifies how well the sets in the collection can be simultaneously balanced. More precisely, we are given an n-point set $P$, and a collection $\mathcal{F} =…

Combinatorics · Mathematics 2017-04-18 Aleksandar Nikolov

Recently, many structured tensors are defined and their properties are discussed in the literature. In this paper, we introduce a new class of structured tensors, called exceptionally regular tensor, which is relevant to the tensor…

Optimization and Control · Mathematics 2015-08-27 Yong Wang , Zheng-Hai Huang , Xue-Li Bai

This paper presents the following results on sets that are complete for NP. 1. If there is a problem in NP that requires exponential time at almost all lengths, then every many-one NP-complete set is complete under length-increasing…

Computational Complexity · Computer Science 2010-02-03 Xiaoyang Gu , John M. Hitchcock , A. Pavan

The canonical polyadic decomposition (CPD) is a fundamental tensor decomposition which expresses a tensor as a sum of rank one tensors. In stark contrast to the matrix case, with light assumptions, the CPD of a low rank tensor is…

Numerical Analysis · Mathematics 2022-02-24 Eric Evert , Michiel Vandecappelle , Lieven De Lathauwer

This paper introduces a new generalized polynomial chaos expansion (PCE) comprising measure-consistent multivariate orthonormal polynomials in dependent random variables. Unlike existing PCEs, whether classical or generalized, no…

Probability · Mathematics 2018-04-17 Sharif Rahman

In this paper, we consider the second-order cone tensor eigenvalue complementarity problem (SOCTEiCP) and present three different reformulations to the model under consideration. Specifically, for the general SOCTEiCP, we first show its…

Optimization and Control · Mathematics 2016-07-26 Jiaojiao Hou , Chen Ling , Hongjin He

The Canonical Polyadic (CP) tensor decomposition is a well-known method for interpretable analysis of high-dimensional data. Recently, the Generalized CP method (GCP) was introduced by Hong and Kolda to allow for flexible choice of the loss…

Numerical Analysis · Mathematics 2026-05-21 Jeremy M. Myers , Eric T. Phipps

In multilinear algebra, some special classes of matrices are extended to higher order structured tensors. The local $w$-uniqueness solution to the linear complementarity problem can be identified by the column competent matrix. Motivated by…

Optimization and Control · Mathematics 2022-04-08 A. Dutta , R. Deb , A. K. Das

This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition. We give an algorithm with provable approximation guarantees for its…

Data Structures and Algorithms · Computer Science 2024-06-19 Mehrdad Ghadiri , Matthew Fahrbach , Gang Fu , Vahab Mirrokni

In this paper, we present a new family of cross $Z$-complementary pairs (CZCPs) based on generalized Boolean functions and two roots of unity. Our key idea is to consider an arbitrary partition of the set $\{1,2,\cdots, n\}$ with two…

Information Theory · Computer Science 2023-08-15 Shibsankar Das , Adrish Banerjee , Zilong Liu

In this paper, we systemically introduce completely positive biquadratic (CPB) tensors and copositive biquadratic tensors. We show that all weakly CPB tensors are sum of squares tensors, the CPB tensor cone and the copositive biquadratic…

Rings and Algebras · Mathematics 2025-10-14 Liqun Qi , Chunfeng Cui , Haibin Chen , Yi Xu

In this work we introduce a mixture of GPs to address the data association problem, i.e. to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has…

Machine Learning · Statistics 2011-08-18 Miguel Lázaro-Gredilla , Steven Van Vaerenbergh , Neil Lawrence
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