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Spectral clustering is a fundamental method for graph partitioning, but its reliance on eigenvector computation limits scalability to massive graphs. Classical sparsification methods preserve spectral properties by sampling edges…

Machine Learning · Computer Science 2025-10-15 Kaiwen He , Petros Drineas , Rajiv Khanna

The constraint satisfaction problem (CSP) involves deciding, given a set of variables and a set of constraints on the variables, whether or not there is an assignment to the variables satisfying all of the constraints. One formulation of…

Computational Complexity · Computer Science 2017-01-09 Hubie Chen , Benoit Larose

Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is still unknown how to perform segmentation from sparse tokens.…

Computer Vision and Pattern Recognition · Computer Science 2023-03-14 Lei Zhou , Huidong Liu , Joseph Bae , Junjun He , Dimitris Samaras , Prateek Prasanna

We present the derivation, implementation, and analysis of a multiresolution adaptive grid framework for numerical simulations on octree-based 3D block-structured collocated grids with distributed computational architectures. Our approach…

Numerical Analysis · Mathematics 2025-04-01 Thomas Gillis , Wim M. van Rees

Motivated by the grid search method and Bayesian optimization, we introduce the concept of contractibility and its applications in model-based optimization. First, a basic framework of contraction methods is established to construct a…

Optimization and Control · Mathematics 2021-08-24 Xiaopeng Luo , Xin Xu

A $\mu$-constrained Boolean Max-CSP$(\psi)$ instance is a Boolean Max-CSP instance on predicate $\psi:\{0,1\}^r \to \{0,1\}$ where the objective is to find a labeling of relative weight exactly $\mu$ that maximizes the fraction of satisfied…

Data Structures and Algorithms · Computer Science 2023-08-21 Suprovat Ghoshal , Euiwoong Lee

A $\left(n,\ell,\gamma\right)$-sharing set family of size $m$ is a family of sets $S_1,\ldots,S_m\subseteq [n]$ s.t. each set has size $\ell$ and each pair of sets shares at most $\gamma$ elements. We let $m\left(n,\ell,\gamma\right)$…

Computational Complexity · Computer Science 2014-04-18 Calvin Beideman , Jeremiah Blocki

We study the compressed sensing (CS) signal estimation problem where an input signal is measured via a linear matrix multiplication under additive noise. While this setup usually assumes sparsity or compressibility in the input signal…

Information Theory · Computer Science 2014-12-23 Junan Zhu , Dror Baron , Marco F. Duarte

We prove that approximating the size of stopping and trapping sets in Tanner graphs of linear block codes, and more restrictively, the class of low-density parity-check (LDPC) codes, is NP-hard. The ramifications of our findings are that…

Information Theory · Computer Science 2008-08-03 Andrew McGregor , Olgica Milenkovic

In this short note we give a new upper bound for the size of a set family with a single Hamming distance. Our proof is an application of the linear algebra bound method.

Combinatorics · Mathematics 2024-09-28 Gábor Hegedüs

We analyse how the standard reductions between constraint satisfaction problems affect their proof complexity. We show that, for the most studied propositional, algebraic, and semi-algebraic proof systems, the classical constructions of…

Computational Complexity · Computer Science 2018-09-26 Albert Atserias , Joanna Ochremiak

In this paper, we study the strength of convex relaxations obtained by convexification of aggregation of constraints for a set $S$ described by two bilinear bipartite equalities. Aggregation is the process of rescaling the original…

Optimization and Control · Mathematics 2024-10-21 Santanu S Dey , Dahye Han , Yang Wang

We study the structure of the solution space and behavior of local search methods on random 3-SAT problems close to the SAT/UNSAT transition. Using the overlap measure of similarity between different solutions found on the same problem…

Statistical Mechanics · Physics 2009-11-13 John Ardelius , Erik Aurell , Supriya Krishnamurthy

The recent work of \cite{CRT,DonohoPol} rigorously proved (in a large dimensional and statistical context) that if the number of equations (measurements in the compressed sensing terminology) in the system is proportional to the length of…

Information Theory · Computer Science 2013-04-02 Mihailo Stojnic

The explosion in the volumes of data being stored online has resulted in distributed storage systems transitioning to erasure coding based schemes. Yet, the codes being deployed in practice are fairly short. In this work, we address what we…

Information Theory · Computer Science 2016-09-22 Parikshit Gopalan , Guangda Hu , Swastik Kopparty , Shubhangi Saraf , Carol Wang , Sergey Yekhanin

Reducing the conditions under which a given set satisfies the stipulations of the subset sum proposition to a set of linear relationships, the question of whether a set satisfies subset sum may be answered in a polynomial number of steps by…

Data Structures and Algorithms · Computer Science 2017-05-16 Aubrey Alston

The Boolean lattice $2^{[n]}$ is the family of all subsets of $[n]=\{1,\dots,n\}$ ordered by inclusion, and a chain is a family of pairwise comparable elements of $2^{[n]}$. Let $s=2^{n}/\binom{n}{\lfloor n/2\rfloor}$, which is the average…

Combinatorics · Mathematics 2019-11-22 Benny Sudakov , Istvan Tomon , Adam Zsolt Wagner

Single domain proteins are thought to be tightly packed. The introduction of voids by mutations is often regarded as destabilizing. In this study we show that packing density for single domain proteins decreases with chain length. We find…

Soft Condensed Matter · Physics 2009-11-10 Jinfeng Zhang , Rong Chen , Chao Tang , Jie Liang

We show that a generalised sparse grid combination technique which combines multi-variate extrapolation of finite difference solutions with the standard combination formula lifts a second order accurate scheme on regular meshes to a fourth…

Numerical Analysis · Mathematics 2026-01-08 Julia Muñoz-Echániz , Christoph Reisinger

We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimization problem. This framework provides…

Machine Learning · Computer Science 2011-06-28 Andreas Argyriou , Luca Baldassarre , Jean Morales , Massimiliano Pontil