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This paper presents a new combinatorial optimisation task, the Subset Sum Matching Problem (SSMP), which is an abstraction of common financial applications such as trades reconciliation. We present three algorithms, two suboptimal and one…

Artificial Intelligence · Computer Science 2025-08-27 Yufei Wu , Manuel R. Torres , Parisa Zehtabi , Alberto Pozanco Lancho , Michael Cashmore , Daniel Borrajo , Manuela Veloso

The boolean satisfiability (SAT) problem asks whether there exists an assignment of boolean values to the variables of an arbitrary boolean formula making the formula evaluate to True. It is well-known that all NP-problems can be coded as…

Machine Learning · Computer Science 2024-10-22 Christopher R. Serrano , Jonathan Gallagher , Kenji Yamada , Alexei Kopylov , Michael A. Warren

We study a linear quadratic optimal control problem with stochastic coefficients and a terminal state constraint, which may be in force merely on a set with positive, but not necessarily full probability. Under such a partial terminal…

Optimization and Control · Mathematics 2017-11-15 Peter Bank , Moritz Voß

In this paper, a novel method to adaptively approximate the solution to stochastic differential equations, which is based on compressive sampling and sparse recovery, is introduced. The proposed method consider the problem of sparse…

Numerical Analysis · Mathematics 2013-07-03 Behrooz Azarkhalili

A convenient framework for dealing with asymptotic limit problems of probabilistic nature is provided. These problems include questions such as finding the asymptotic proportion of terms of a sequence falling inside a given interval, or the…

History and Overview · Mathematics 2024-04-08 Michaël Bensimhoun

Stochastic versions of proximal methods have gained much attention in statistics and machine learning. These algorithms tend to admit simple, scalable forms, and enjoy numerical stability via implicit updates. In this work, we propose and…

Machine Learning · Statistics 2024-09-09 Haoyu Jiang , Jason Xu

We propose an algorithm for solving bound-constrained mathematical programs with complementarity constraints on the variables. Each iteration of the algorithm involves solving a linear program with complementarity constraints in order to…

Optimization and Control · Mathematics 2022-01-14 Christian Kirches , Jeffrey Larson , Sven Leyffer , Paul Manns

Article presents the compatibility matrix method and illustrates it with the application to P vs NP problem. The method is a generalization of descriptive geometry: in the method, we draft problems and solve them utilizing the image…

Computational Complexity · Computer Science 2012-05-08 Sergey Gubin

The aim of the paper is to answer a long-standing open problem on the relationship between NP and BQP. The paper shows that BQP contains NP by proposing a BQP quantum algorithm for the MAX-E3-SAT problem which is a fundamental NP-hard…

Computational Complexity · Computer Science 2015-07-28 Ahmed Younes , Jonathan E. Rowe

Obtaining lower bounds for NP-hard problems has for a long time been an active area of research. Recent algebraic techniques introduced by Jonsson et al. (SODA 2013) show that the time complexity of the parameterized SAT($\cdot$) problem…

Computational Complexity · Computer Science 2014-06-13 Peter Jonsson , Victor Lagerkvist , Johannes Schmidt , Hannes Uppman

1-in-3 SAT is an NP-complete variant of 3-SAT\ where a "clause" is satisfied iff exactly one of its three literal is satisfied. We present here an exact algorithm solving \oit\ in time $O^*(1.260^n)$.

Computational Complexity · Computer Science 2013-07-30 Édouard Bonnet , Vangelis Th. Paschos

The Promise Constraint Satisfaction Problem (PCSP) is a generalization of the Constraint Satisfaction Problem (CSP) that includes approximation variants of satisfiability and graph coloring problems. Barto [LICS '19] has shown that a…

Computational Complexity · Computer Science 2025-06-09 Kristina Asimi , Libor Barto

We present a logical framework to represent and reason about stochastic optimization problems based on probability answer set programming. This is established by allowing probability optimization aggregates, e.g., minimum and maximum in the…

Artificial Intelligence · Computer Science 2013-04-15 Emad Saad

We describe a novel optimization method for finite sums (such as empirical risk minimization problems) building on the recently introduced SAGA method. Our method achieves an accelerated convergence rate on strongly convex smooth problems.…

Machine Learning · Statistics 2016-10-31 Aaron Defazio

We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is strongly convex. While standard stochastic gradient methods converge at sublinear rates for this problem, the proposed…

Optimization and Control · Mathematics 2013-03-12 Nicolas Le Roux , Mark Schmidt , Francis Bach

A novel approach to complex problems has been previously applied to graph classification and the graph equivalence problem. Here we apply it to the NP complete problem of finding the largest perfect clique within a graph $G$.

Condensed Matter · Physics 2007-05-23 Vladimir Gudkov , Shmuel Nussinov , Zohar Nussinov

Earlier we introduced the notion of a stable set of points (SSP). We proved that a CNF formula is unsatisfiable iff there is a set of points (i.e. complete assignments) that is stable with respect to this formula. Experiments showed that…

Logic in Computer Science · Computer Science 2025-07-18 Eugene Goldberg

In this paper we suggest analytical methods and associated algorithms for determining the sum of the subsets $X_m$ of the set $X_n$ (subset sum problem). Our algorithm has time complexity $T=O(C_{n}^{k})$ ($k=[m/2]$, which significantly…

Information Theory · Computer Science 2020-05-05 B. Sinchev , A. B. Sinchev , J. Akzhanova , A. M. Mukhanova , Y. Issekeshev

We study a family of optimal control problems under a set of controlled-loss constraints holding at different deterministic dates. The characterization of the associated value function by a Hamilton-Jacobi-Bellman equation usually calls for…

Optimization and Control · Mathematics 2020-07-27 Geraldine Bouveret , Athena Picarelli

We tackle a stochastic version of the Critical Node Problem (CNP) where the goal is to minimize the pairwise connectivity of a graph by attacking a subset of its nodes. In the stochastic setting considered, the attacks on nodes can fail…

Data Structures and Algorithms · Computer Science 2019-05-30 Pierre Hosteins , Rosario Scatamacchia