2024 Maximizing non-monotone submodular functions

Maximizing non-monotone submodular functions

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Web4 mrt. 2015 · The problem of maximizing non-negative monotone submodular functions under a certain constraint has been intensively studied in the last decade. In this paper, we address the problem for functions defined over the integer lattice. Suppose that a non-negative monotone submodular function is given via an evaluation oracle. http://proceedings.mlr.press/v80/bai18a/bai18a.pdf

Non-monotone Submodular Maximization under Matroid and …

Webgeneral non-monotone submodular objectives. In this work we present a new uniﬁed continuous greedy algorithm which ﬁnds approximate fractional solutions for both the non-monotone and monotone cases, and improves on the approximation ratio for many applications. For general non-monotone submodular objective functions, WebOn non-monotone submodular functions Lee et al. [37] provided a 5-approximation algorithm for ... Maximizing non-monotone submodular set functions subject to … country bars in houston tx

Submodular set function - Wikipedia

Webfor maximizing non-monotone k-submodular functions with individual size constraints [SGW20]. Tang, Wang, and Chan propose an (1 2− 1 2e)-approximation algorithm for … Web20 nov. 2024 · As many combinatorial optimization problems also involve non-monotone or non-submodular objective functions, we consider these two general problem classes, maximizing non-monotone submodular functions without constraints and maximizing monotone non-submodular functions with a size constraint. Webof maximizing submodular and non-submodular functions on the integer lattice has received a lot of recent attention. In this paper, we study streaming algorithms for the … brett haddock beats wife

On maximizing monotone or non-monotone k-submodular …

Maximizing a monotone non-submodular function under a …

WebOn non-monotone submodular functions Lee et al. [37] provided a 5-approximation algorithm for kknapsack constraints, which was the ﬁrst constant factor algorithm for the problem. Fadaei et al. [19] building on the approach of Lee et al. [37], reduced this factor to 4. One of the most interesting WebThe combination for all buyers is a non-monotone submodular function. It also is non-negative at~0 and~1, by extending the model and accounting for extra revenue gains from buyers with free trials. Our Results. Maximizing a submodular function over the hypercube is at least as difﬁcult as over brett hadley actorWeb1 jan. 2024 · In this note, we study the maximization problem of a non-negative monotone k -submodular function under a knapsack constraint, and give a deterministic -approximation algorithm (see Theorem 1 ). It is an adaption to Sviridenko's -approximation algorithm for submodular knapsack maximization [14]. Related works. brett hack vasculitis charities

"Web4 mrt. 2015 · The problem of maximizing non-negative monotone submodular functions under a certain constraint has been intensively studied in the last decade. In this paper, … " - Maximizing non-monotone submodular functions

Maximizing non-monotone submodular functions

Maximizing Monotone Submodular Functions over the Integer …

Web20 sep. 2014 · This work considers the problem of maximizing a non-negative symmetric submodular function f:2N → R+ subject to a down-monotone solvable polytope P ⊆ [0, 1]N and describes a deterministic linear-time 1/2-approximation algorithm solution. Symmetric submodular functions are an important family of submodular functions … Web1 jan. 2024 · 1. Introduction. A k -submodular function is a generalization of submodular function, where the input consists of k disjoint subsets of the domain, instead of a single …

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Web16 nov. 2024 · Optimization of submodular functions is a central topic in the field of combinatorial optimization, operations research, economics, and especially machine … WebWeak submodularity is a natural relaxation of the diminishing return property, which is equivalent to submodularity. Weak submodularity has been used to show that many (monotone) functions that arise in practice can be…

Web17 jul. 2024 · 3 The greedy algorithm for maximizing a monotone non-submodular function under a knapsack constraint We present the greedy algorithm for ( 1) as … Weba monotone submodular function g and a linear function ℓ. Motivated by the above applications, Sviridenko et al. [17] also initialized the study of the optimization of g + ℓ sums. In particular, they described algorithms with optimal approximation guarantees for this problem when g is a non-negative monotone submodular function, ℓ is a linear

Web-approximation algorithm for maximizing non-monotone and monotone k-submodular functions, respectively, when there is no constraint. Organization: The rest of this paper … Web12 apr. 2024 · A k-submodular function is a generalization of a submodular function. The definition domain of a k-submodular function is a collection of k-disjoint subsets instead of simple subsets of ground set. In this paper, we consider the maximization of a k-submodular function with the intersection of a knapsack and m matroid constraints. …

Web27 mrt. 2024 · 2024. TLDR. This work introduces a decreasing threshold greedy algorithm with a binary search as its subroutine to solve the problem of maximizing the sum of a …

WebReview 2. Summary and Contributions: The paper considers the problem of maximizing a (not necessarily monotone) submodular function subject to a knapsack constraint in … country bars in fort worthWeb1 nov. 2024 · The problem of maximizing non-negative monotone submodular functions under a certain constraint has been intensively studied in the last decade. In this paper, we address the problem for functions defined over the integer lattice. brett haderlie chiropracticWeb11 feb. 2024 · In the problem of maximizing non-monotone k -submodular function f under individual size constraints, the goal is to maximize the value of k disjoint subsets with … country bars in marylandWeb13 apr. 2024 · In the set function optimization, the analysis on submodular functions, especially on monotone nondecreasing submodular functions, has been very well studied , so that although maximization and constrained minimization of submodular functions are often NP-hard, there exist many theoretical guaranteed approximation solutions for … brett hadley wikipediaWeb4 nov. 2024 · DR-submodular continuous functions are important objectives with wide real-world applications spanning MAP inference in determinantal point processes … country bars in illinoisWebSubmodular set function maximization. Unlike the case of minimization, maximizing a generic submodular function is NP-hard even in the unconstrained setting. Thus, most of … country bars in jacksonville flWebmaximizing submodular functions is NP-hard. In this paper, we design the rst constant-factor approximation algorithms for maximizing non-negative (non-monotone) … brett hadley howard stern