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A fundamental task underlying many important optimization problems, from influence maximization to sensor placement to content recommendation, is to select the optimal group of $k$ items from a larger set. Submodularity has been very…

Data Structures and Algorithms · Computer Science 2022-03-02 Jon Kleinberg , Emily Ryu , Éva Tardos

We develop an interpolation-based modeling framework for parameter-dependent partial differential equations arising in control, inverse problems, and uncertainty quantification. The solution is discretized in the physical domain using…

Numerical Analysis · Mathematics 2026-04-20 Erik Burman , Mats G. Larson , Karl Larsson , Jonatan Vallin

In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves the running time for constrained submodular maximization but…

Machine Learning · Computer Science 2020-02-11 Ashwinkumar Badanidiyuru , Amin Karbasi , Ehsan Kazemi , Jan Vondrak

Using polarity, we give an outer polyhedral approximation for the epigraph of set functions. For a submodular function, we prove that the corresponding polar relaxation is exact; hence, it is equivalent to the Lov\'asz extension. The polar…

Optimization and Control · Mathematics 2020-12-09 Alper Atamturk , Vishnu Narayanan

It is a well-known result of Etingof, Nikshych and Ostrik that there are finitely many inequivalent integral modular categories of any fixed rank $n$. This follows from a double-exponential bound on the maximal denominator in an Egyptian…

Quantum Algebra · Mathematics 2010-12-09 Paul Bruillard , Eric C. Rowell

We study the complexity of approximate representation and learning of submodular functions over the uniform distribution on the Boolean hypercube $\{0,1\}^n$. Our main result is the following structural theorem: any submodular function is…

Machine Learning · Computer Science 2013-04-03 Vitaly Feldman , Pravesh Kothari , Jan Vondrak

Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper we study this problem in a dynamic environment with…

Data Structures and Algorithms · Computer Science 2024-05-31 Paul Dütting , Federico Fusco , Silvio Lattanzi , Ashkan Norouzi-Fard , Morteza Zadimoghaddam

We present Binomials, a package for the computer algebra system Macaulay2, which specializes well known algorithms to binomial ideals. These come up frequently in algebraic statistics and commutative algebra, and it is shown that…

Commutative Algebra · Mathematics 2016-04-08 Thomas Kahle

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

We study the set of monomial ideals in a polynomial ring as an ordered set, with the ordering given by reverse inclusion. We give a short proof of the fact that every antichain of monomial ideals is finite. Then we investigate ordinal…

Logic · Mathematics 2007-05-23 Matthias Aschenbrenner , Wai-Yan Pong

The objective of ordinal embedding is to find a Euclidean representation of a set of abstract items, using only answers to triplet comparisons of the form "Is item $i$ closer to the item $j$ or item $k$?". In recent years, numerous…

Machine Learning · Computer Science 2021-10-22 Leena Chennuru Vankadara , Siavash Haghiri , Michael Lohaus , Faiz Ul Wahab , Ulrike von Luxburg

Minimizing a sum of simple submodular functions of limited support is a special case of general submodular function minimization that has seen numerous applications in machine learning. We develop fast techniques for instances where…

Machine Learning · Computer Science 2021-10-29 Nate Veldt , Austin R. Benson , Jon Kleinberg

In this article, we construct semiparametrically efficient estimators of linear functionals of a probability measure in the presence of side information using an easy empirical likelihood approach. We use estimated constraint functions and…

Methodology · Statistics 2023-03-01 Shan Wang , Hanxiang Peng

Symmetric submodular functions are an important family of submodular functions capturing many interesting cases including cut functions of graphs and hypergraphs. Maximization of such functions subject to various constraints receives little…

Data Structures and Algorithms · Computer Science 2016-04-19 Moran Feldman

When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabilities, we…

Machine Learning · Computer Science 2024-05-07 Kensen Shi , Joey Hong , Yinlin Deng , Pengcheng Yin , Manzil Zaheer , Charles Sutton

Regular chains and triangular decompositions are fundamental and well-developed tools for describing the complex solutions of polynomial systems. This paper proposes adaptations of these tools focusing on solutions of the real analogue:…

Symbolic Computation · Computer Science 2010-05-17 Changbo Chen , James H. Davenport , John P. May , Marc Moreno Maza , Bican Xia , Rong Xiao

This is a thesis that was defended in 2009 at Lomonosov Moscow State University. In Chapter 1: 1. It is proved that that the class of lower (Skolem) elementary functions is the set of all polynomial-bounded functions that can be obtained by…

Computational Complexity · Computer Science 2016-11-22 Sergey Volkov

We investigate the descriptional complexity of operations on semilinear sets. Roughly speaking, a semilinear set is the finite union of linear sets, which are built by constant and period vectors. The interesting parameters of a semilinear…

Formal Languages and Automata Theory · Computer Science 2017-08-23 Simon Beier , Markus Holzer , Martin Kutrib

Hypergraphs are structures that can be decomposed or described; in other words they are recursively countable. Here, we get exact and asymptotic enumeration results on hypergraphs by means of exponential generating functions. The number of…

Discrete Mathematics · Computer Science 2008-06-20 Tsiriniaina Andriamampianina

We analyse and compare the complexity of several algorithms for computing modular polynomials. We show that an algorithm relying on floating point evaluation of modular functions and on interpolation, which has received little attention in…

Number Theory · Mathematics 2009-05-08 Andreas Enge