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Submodularity is an important concept in integer and combinatorial optimization. A classical submodular set function models the utility of selecting homogenous items from a single ground set, and such selections can be represented by binary…

Optimization and Control · Mathematics 2023-04-06 Simge Küçükyavuz , Qimeng Yu

Submodular functions, as well as the sub-class of decomposable submodular functions, and their optimization appear in a wide range of applications in machine learning, recommendation systems, and welfare maximization. However, optimization…

Data Structures and Algorithms · Computer Science 2024-06-04 Akbar Rafiey

In this paper we consider quasi-concave set functions defined on antimatroids. There are many equivalent axiomatizations of antimatroids, that may be separated into two categories: antimatroids defined as set systems and antimatroids…

Combinatorics · Mathematics 2011-01-25 Vadim E. Levit , Yulia Kempner

We consider active, semi-supervised learning in an offline transductive setting. We show that a previously proposed error bound for active learning on undirected weighted graphs can be generalized by replacing graph cut with an arbitrary…

Machine Learning · Computer Science 2012-02-20 Andrew Guillory , Jeff A. Bilmes

To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposals. In structured…

Machine Learning · Computer Science 2014-11-10 Adarsh Prasad , Stefanie Jegelka , Dhruv Batra

Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of…

Machine Learning · Computer Science 2017-12-07 Daniel Golovin , Andreas Krause

Via a family of monotone scalar functions, a preorder on a set is extended to its power set and then used to construct a hull operator and a corresponing complete lattice of sets. A function mappping into the preordered set is extended to a…

Optimization and Control · Mathematics 2018-12-11 Giovanni Crespi , Andreas H Hamel , Matteo Rocca , Carola Schrage

Submodular function maximization has been studied extensively in recent years under various constraints and models. The problem plays a major role in various disciplines. We study a natural online variant of this problem in which elements…

Data Structures and Algorithms · Computer Science 2015-01-26 Niv Buchbinder , Moran Feldman , Roy Schwartz

Constrained submodular set function maximization problems often appear in multi-agent decision-making problems with a discrete feasible set. A prominent example is the problem of multi-agent mobile sensor placement over a discrete domain.…

Optimization and Control · Mathematics 2020-12-01 Navid Rezazadeh , Solmaz S. Kia

Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimization problems. During the past two decades, promising…

Neural and Evolutionary Computing · Computer Science 2022-11-29 Chao Qian , Yang Yu , Ke Tang , Xin Yao , Zhi-Hua Zhou

We study submodular maximization problems with matroid constraints, in particular, problems where the objective can be expressed via compositions of analytic and multilinear functions. We show that for functions of this form, the so-called…

Machine Learning · Computer Science 2024-12-17 Gözde Özcan , Armin Moharrer , Stratis Ioannidis

Optimization problems, generalized equations, and the multitude of other variational problems invariably lead to the analysis of sets and set-valued mappings as well as their approximations. We review the central concept of set-convergence…

Optimization and Control · Mathematics 2020-02-25 Johannes O. Royset

This paper investigates connections between discrete and continuous approaches for decomposable submodular function minimization. We provide improved running time estimates for the state-of-the-art continuous algorithms for the problem…

Machine Learning · Computer Science 2017-03-07 Alina Ene , Huy L. Nguyen , László A. Végh

Submodular function minimization (SFM) is a fundamental and efficiently solvable problem class in combinatorial optimization with a multitude of applications in various fields. Surprisingly, there is only very little known about constraint…

Data Structures and Algorithms · Computer Science 2018-11-27 Martin Nägele , Benny Sudakov , Rico Zenklusen

The submodular partitioning problem asks to minimize, over all partitions $P$ of a ground set $V$, the sum of a given submodular function $f$ over the parts of $P$. The problem has seen considerable work in approximability, as it…

Data Structures and Algorithms · Computer Science 2025-07-03 Kristóf Bérczi , Karthekeyan Chandrasekaran , Tamás Király , Daniel P. Szabo

We employ the notions of `sequential function' and `interrogation' (dialogue) in order to define new partial combinatory algebra structures on sets of functions. These structures are analyzed using J. Longley's preorder-enriched category of…

Logic · Mathematics 2009-05-19 Jaap van Oosten

Problems of segmentation, denoising, registration and 3D reconstruction are often addressed with the graph cut algorithm. However, solving an unconstrained graph cut problem is NP-hard. For tractable optimization, pairwise potentials have…

Machine Learning · Computer Science 2019-11-26 Maxim Berman , Matthew B. Blaschko

In this work we classify the at-point regularities of set-valued mappings into two categories and then we analyze their relationship through several implications and examples. After this theoretical tour, we use the subregularity properties…

Optimization and Control · Mathematics 2012-02-07 Marius Apetrii , Marius Durea , Radu Strugariu

The paper is devoted to developing subdifferential theory for set-valued mappings taking values in ordered infinite-dimensional spaces. This study is motivated by applications to problems of vector and set optimization with various…

Optimization and Control · Mathematics 2024-10-16 Boris S. Mordukhovich , Oanh Nguyen

Complementable operators extend classical matrix decompositions, such as the Schur complement, to the setting of infinite-dimensional Hilbert spaces, thereby broadening their applicability in various mathematical and physical contexts. This…

Functional Analysis · Mathematics 2025-01-14 Sachin Manjunath Naik , P. Sam Johnson