Polyhedrization of Discrete Convex Volumes

2006 ◽  
pp. 548-557
Author(s):  
Valentin E. Brimkov ◽  
Reneta Barneva
Keyword(s):  
Discrete Convex

Discrete Convex Analysis and Its Applications in Operations: A Survey

2019 ◽  
Author(s):  
Xin Chen ◽  
Menglong Li
Keyword(s):  
Convex Analysis ◽  
Discrete Convex Analysis ◽  
Discrete Convex

Discrete Convex Simulation Optimization

2020 ◽  
Author(s):  
Haixiang Zhang ◽  
Zeyu Zheng
Keyword(s):  
Simulation Optimization ◽  
Discrete Convex

scholarly journals STABILITY IN SUPPLY CHAIN NETWORKS: AN APPROACH BY DISCRETE CONVEX ANALYSIS

2015 ◽  
Vol 58 (3) ◽  
pp. 271-290 ◽  
Author(s):  
Yoshiko T Ikebe ◽  
Akihisa Tamura
Keyword(s):  
Supply Chain ◽  
Convex Analysis ◽  
Supply Chain Networks ◽  
Discrete Convex Analysis ◽  
Discrete Convex

scholarly journals ON THE PIPAGE ROUNDING ALGORITHM FOR SUBMODULAR FUNCTION MAXIMIZATION — A VIEW FROM DISCRETE CONVEX ANALYSIS

2009 ◽  
Vol 01 (01) ◽  
pp. 1-23 ◽  
Author(s):  
AKIYOSHI SHIOURA
Keyword(s):  
Approximation Algorithm ◽  
Convex Analysis ◽  
Submodular Function ◽  
Submodular Functions ◽  
Concave Functions ◽  
Discrete Convex Analysis ◽  
Discrete Convex ◽  
Weighted Rank ◽  
Rounding Algorithm ◽  
Rank Functions

We consider the problem of maximizing a nondecreasing submodular set function under a matroid constraint. Recently, Calinescu et al. (2007) proposed an elegant framework for the approximation of this problem, which is based on the pipage rounding technique by Ageev and Sviridenko (2004), and showed that this framework indeed yields a (1 - 1/e)-approximation algorithm for the class of submodular functions which are represented as the sum of weighted rank functions of matroids. This paper sheds a new light on this result from the viewpoint of discrete convex analysis by extending it to the class of submodular functions which are the sum of M ♮-concave functions. M ♮-concave functions are a class of discrete concave functions introduced by Murota and Shioura (1999), and contain the class of the sum of weighted rank functions as a proper subclass. Our result provides a better understanding for why the pipage rounding algorithm works for the sum of weighted rank functions. Based on the new observation, we further extend the approximation algorithm to the maximization of a nondecreasing submodular function over an integral polymatroid. This extension has an application in multi-unit combinatorial auctions.

Optimal Algorithm for Solution of Discrete Convex Combinations Problem

2006 ◽  
Vol 27 ◽  
pp. 61-62
Author(s):  
Alexander G. Kolpakov ◽  
A.A. Kolpakov
Keyword(s):  
Optimal Algorithm ◽  
Discrete Convex

scholarly journals On asymptotics of the discrete convex LSE of a p.m.f.

Bernoulli ◽  
2017 ◽  
Vol 23 (3) ◽  
pp. 1449-1480 ◽  
Author(s):  
Fadoua Balabdaoui ◽  
Cécile Durot ◽  
François Koladjo
Keyword(s):  
Discrete Convex
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