Results 11 to 20 of about 10,146,366 (200)
Monotonic Decompositions of Submodular Set Functions [PDF]
SIAM Journal on Discrete MathematicsAbstract. Submodular set functions are undoubtedly among the most important building blocks of combinatorial optimization. Somewhat surprisingly, continuous counterparts of such functions have also appeared in an analytic line of research where they found applications in the theory of finitely additive measures, nonlinear integrals ...
Kristóf Bérczi +4 more
core +9 more sources
Convex Analysis for Minimizing and Learning Submodular Set Functions [PDF]
, 2013 The connections between convexity and submodularity are explored, for purposes of minimizing and learning submodular set functions. First, we develop a novel method for minimizing a particular class of submodular functions, which can be expressed as a sum of concave functions composed with modular functions.
Peter Stobbe, Stobbe, Peter
openaire +2 more sources
Minimizing submodular functions over families of sets
Combinatorica, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michel X. Goemans, V. S. Ramakrishnan
openaire +4 more sources
Generalized budgeted submodular set function maximization [PDF]
Information and Computation, 2021 In this paper we consider a generalization of the well-known budgeted maximum coverage problem. We are given a ground set of elements and a set of bins. The goal is to find a subset of elements along with an associated set of bins, such that the overall cost is at most a given budget, and the profit is maximized.
Francesco Cellinese +3 more
openaire +12 more sources
Ranking with submodular functions on a budget. [PDF]
Data Min Knowl Discov, 2022 Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more.
Zhang G, Tatti N, Gionis A.
europepmc +2 more sources
Distributed strategy selection: A submodular set function maximization approach
Automatica, 2023 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. Submodular set function optimization problems, however, are known to be NP-hard.
Navid Rezazadeh, Solmaz S. Kia
openaire +5 more sources
Hardness of submodular cost allocation : lattice matching and a simplex coloring conjecture [PDF]
, 2014 We consider the Minimum Submodular Cost Allocation (MSCA) problem. In this problem, we are given k submodular cost functions f1, ... , fk: 2V -> R+ and the goal is to partition V into k sets A1, ..., Ak so as to minimize the total cost sumi = 1,k fi(Ai).
Vondrák, Jan, Ene, Alina
core +1 more source
Single Machine Vector Scheduling with General Penalties
Mathematics, 2021 In this paper, we study the single machine vector scheduling problem (SMVS) with general penalties, in which each job is characterized by a d-dimensional vector and can be accepted and processed on the machine or rejected.
Xiaofei Liu, Weidong Li, Yaoyu Zhu
doaj +1 more source
Monotone Submodular Maximization over a Matroid via Non-Oblivious Local Search [PDF]
, 2013 We present an optimal, combinatorial 1−1/e approximation algorithm for monotone submodular optimization over a matroid constraint. Compared to the continuous greedy algorithm (Calinescu, Chekuri, Pál and Vondrák, 2008), our algorithm is extremely simple ...
Filmus, Yuval +3 more
core +1 more source
Constrained Path Search with Submodular Function Maximization [PDF]
, 2022 In this paper, we study the problem of constrained path search with submodular function maximization (CPS-SM). We aim to find the path with the best submodular function score under a given constraint (e.g., a length limit), where the submodular function ...
Fang, Yixiang +6 more
core +1 more source