Online Algorithms for Covering and Packing Problems with Convex Objectives

Conference Paper

We present online algorithms for covering and packing problems with (non-linear) convex objectives. The convex covering problem is defined as: minxαRn+f(x) s.t. Ax ≥ 1, where f:Rn+ → R+ is a monotone convex function, and A is an m×n matrix with non-negative entries. In the online version, a new row of the constraint matrix, representing a new covering constraint, is revealed in each step and the algorithm is required to maintain a feasible and monotonically non-decreasing assignment x over time. We also consider a convex packing problem defined as: maxyαRm+ Σ mj=1 yj - g(AT y), where g:Rn+→R+ is a monotone convex function. In the online version, each variable yj arrives online and the algorithm must decide the value of yj on its arrival. This represents the Fenchel dual of the convex covering program, when g is the convex conjugate of f. We use a primal-dual approach to give online algorithms for these generic problems, and use them to simplify, unify, and improve upon previous results for several applications.

Full Text

Duke Authors

Cited Authors

  • Azar, Y; Buchbinder, N; Chan, THH; Chen, S; Cohen, IR; Gupta, A; Huang, Z; Kang, N; Nagarajan, V; Naor, J; Panigrahi, D

Published Date

  • December 14, 2016

Published In

Volume / Issue

  • 2016-December /

Start / End Page

  • 148 - 157

International Standard Serial Number (ISSN)

  • 0272-5428

International Standard Book Number 13 (ISBN-13)

  • 9781509039333

Digital Object Identifier (DOI)

  • 10.1109/FOCS.2016.24

Citation Source

  • Scopus