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In this paper we study submodular maximization under a matroid constraint in the adaptive complexity model. This model was recently introduced in the context of submodular optimization in [BS18a] to quantify the information theoretic…

Data Structures and Algorithms · Computer Science 2018-11-09 Eric Balkanski , Aviad Rubinstein , Yaron Singer

This paper discusses the shortest path problem in a general directed graph with $n$ nodes and $K$ cost scenarios (objectives). In order to choose a solution, the min-max criterion is applied. The min-max version of the problem is hard to…

Data Structures and Algorithms · Computer Science 2024-09-18 Adam Kasperski , Pawel Zielinski

The goal in {\em reconfiguration problems} is to compute a {\em gradual transformation} between two feasible solutions of a problem such that all intermediate solutions are also feasible. In the {\em Matching Reconfiguration Problem} (MRP),…

Data Structures and Algorithms · Computer Science 2020-05-07 Noam Solomon , Shay Solomon

In this work we revisit the elementary scheduling problem $1||\sum p_j U_j$. The goal is to select, among $n$ jobs with processing times and due dates, a subset of jobs with maximum total processing time that can be scheduled in sequence…

Data Structures and Algorithms · Computer Science 2025-07-23 Nick Fischer , Leo Wennmann

In numerical linear algebra, considerable effort has been devoted to obtaining faster algorithms for linear systems whose underlying matrices exhibit structural properties. A prominent success story is the method of generalized nested…

Data Structures and Algorithms · Computer Science 2023-10-26 Sally Dong , Gramoz Goranci , Lawrence Li , Sushant Sachdeva , Guanghao Ye

We study the maximum set coverage problem in the massively parallel model. In this setting, $m$ sets that are subsets of a universe of $n$ elements are distributed among $m$ machines. In each round, these machines can communicate with each…

Data Structures and Algorithms · Computer Science 2024-11-26 Thai Bui , Hoa T. Vu

A polynomial optimization problem (POP) asks for minimizing a polynomial function given a finite set of polynomial constraints (equations and inequalities). This problem is well-known to be hard in general, as it encodes many hard…

Optimization and Control · Mathematics 2024-01-24 Luis Felipe Vargas

We consider infinite-horizon $\gamma$-discounted (linear) constrained Markov decision processes (CMDPs) where the objective is to find a policy that maximizes the expected cumulative reward subject to expected cumulative constraints. Given…

Machine Learning · Computer Science 2025-10-29 Xingtu Liu , Lin F. Yang , Sharan Vaswani

Non-commutative polynomial optimization (NPO) problems seek to minimize the state average of a polynomial of some operator variables, subject to polynomial constraints, over all states and operators, as well as the Hilbert spaces where…

Quantum Physics · Physics 2025-07-22 Mateus Araújo , Andrew J. P. Garner , Miguel Navascues

The min-distance between two nodes $u, v$ is defined as the minimum of the distance from $v$ to $u$ or from $u$ to $v$, and is a natural distance metric in DAGs. As with the standard distance problems, the Strong Exponential Time Hypothesis…

Data Structures and Algorithms · Computer Science 2022-10-05 Mina Dalirrooyfard , Jenny Kaufmann

This paper introduces a novel algorithm for Mixed-Integer Nonlinear Programming (MINLP) problems with multilinear interpolations of look-up tables. These problems arise when objective or constraints contain black-box functions only known at…

This paper addresses the challenge of solving Constrained Markov Decision Processes (CMDPs) with $d > 1$ constraints when the transition dynamics are unknown, but samples can be drawn from a generative model. We propose a model-based…

Machine Learning · Computer Science 2025-03-11 Max Buckley , Konstantinos Papathanasiou , Andreas Spanopoulos

Submodular functions are set functions mapping every subset of some ground set of size $n$ into the real numbers and satisfying the diminishing returns property. Submodular minimization is an important field in discrete optimization theory…

Data Structures and Algorithms · Computer Science 2020-01-16 Yassine Hamoudi , Patrick Rebentrost , Ansis Rosmanis , Miklos Santha

The problem of solving linear systems is one of the most fundamental problems in computer science, where given a satisfiable linear system $(A,b)$, for $A \in \mathbb{R}^{n \times n}$ and $b \in \mathbb{R}^n$, we wish to find a vector $x…

Data Structures and Algorithms · Computer Science 2021-06-25 Mitali Bafna , Nikhil Vyas

We solve the problem of minimizing the number of critical points among all functions on a surface within a prescribed distance {\delta} from a given input function. The result is achieved by establishing a connection between discrete Morse…

Computational Geometry · Computer Science 2015-05-07 Ulrich Bauer , Carsten Lange , Max Wardetzky

We consider finding a zero point of the maximally monotone operator $T$. First, instead of using the proximal point algorithm (PPA) for this purpose, we employ PPA to solve its Yosida regularization $T_{\lambda}$. Then, based on an…

Optimization and Control · Mathematics 2023-12-25 Tao Zhang , Shiru Li , Yong Xia

In this paper, we propose an efficient numerical approach for solving a specific type of quartic inhomogeneous polynomial optimization problem inspired by practical applications. The primary contribution of this work lies in establishing an…

Optimization and Control · Mathematics 2026-01-01 Haibin Chen , Yixuan Chen , Chunyan Wang , Qi Fan

We present a factor $14D^2$ approximation algorithm for the minimum linear arrangement problem on series-parallel graphs, where $D$ is the maximum degree in the graph. Given a suitable decomposition of the graph, our algorithm runs in time…

Discrete Mathematics · Computer Science 2014-10-17 Martina Eikel , Christian Scheideler , Alexander Setzer

We consider the problem of robust matrix completion, which aims to recover a low rank matrix $L_*$ and a sparse matrix $S_*$ from incomplete observations of their sum $M=L_*+S_*\in\mathbb{R}^{m\times n}$. Algorithmically, the robust matrix…

Machine Learning · Statistics 2020-03-25 Yunfeng Cai , Ping Li

The Discrete Morse Theory of Forman appeared to be useful for providing filtration-preserving reductions of complexes in the study of persistent homology. So far, the algorithms computing discrete Morse matchings have only been used for…

Computational Geometry · Computer Science 2015-03-13 Madjid Allili , Tomasz Kaczynski , Claudia Landi