Section: New Results

Discrete Optimization Algorithms

Shrinking Maxima, Decreasing Costs: New Online Packing and Covering Problems

Participants : Pierre Fraigniaud, Magnús M. Halldórsson, Boaz Patt-Shamir, Dror Rawitz, Adi Rosén.

We consider in [23] , two new variants of online integer programs that are duals. In the packing problem we are given a set of items and a collection of knapsack constraints over these items that are revealed over time in an online fashion. Upon arrival of a constraint we may need to remove several items (irrevocably) so as to maintain feasibility of the solution. Hence, the set of packed items becomes smaller over time. The goal is to maximize the number, or value, of packed items. The problem originates from a buffer-overflow model in communication networks, where items represent information units broken into multiple packets. The other problem considered is online covering: There is a universe to be covered. Sets arrive online, and we must decide for each set whether we add it to the cover or give it up. The cost of a solution is the total cost of sets taken, plus a penalty for each uncovered element. The number of sets in the solution grows over time, but its cost goes down. This problem is motivated by team formation, where the universe consists of skills, and sets represent candidates we may hire. The packing problem was introduced for the special case where the matrix is binary; in this paper we extend the solution to general matrices with non-negative integer entries. The covering problem is introduced in this paper; we present matching upper and lower bounds on its competitive ratio.

Generalized Subdifferentials of the Sign Change Counting Function

Participants : Dominique Fortin, Ider Tseveendorj.

A natural generalization of piecewise linear approximation of non convex problems relies on piecewise convex approximation; along the way to solve the piecewise convex maximization problem [30] both effectively and efficiently, optimality conditions have to be addressed in two ways: either the violation of necessary conditions should lead to a direction of improvement from a local solution, or a sufficient condition for global optimality has to be fulfilled. The way to either goal is paved with subdifferentials and their generalizations on a per problem basis.

In the article [29] , the counting function on binary values is extended to the signed case in order to count the number of transitions between contiguous locations. A generalized subdifferential for the sign change counting function is given where classical subdifferentials remain intractable. An attempt to prove global optimality at some point, for the 4-dimensional first non trivial example, is made by using a sufficient condition specially tailored among all the cases for this subdifferential.