The Alternating Stock Size Problem and the Gasoline Puzzle

TL;DR

This paper introduces the alternating stock size problem, a variant of the stock size problem with an added alternating constraint, providing improved approximation algorithms and linking it to the gasoline puzzle, with practical applications.

Contribution

It presents a 1.79-approximation algorithm for the alternating stock size problem and a 2-approximation for the gasoline puzzle, using novel linear programming rounding techniques.

Findings

Achieved a 1.79 approximation ratio for the alternating stock size problem.

Developed a 2-approximation algorithm for the gasoline puzzle.

Established a close relationship between the alternating stock size problem and the gasoline puzzle.

Abstract

Given a set S of integers whose sum is zero, consider the problem of finding a permutation of these integers such that: (i) all prefix sums of the ordering are nonnegative, and (ii) the maximum value of a prefix sum is minimized. Kellerer et al. referred to this problem as the "Stock Size Problem" and showed that it can be approximated to within 3/2. They also showed that an approximation ratio of 2 can be achieved via several simple algorithms. We consider a related problem, which we call the "Alternating Stock Size Problem", where the number of positive and negative integers in the input set S are equal. The problem is the same as above, but we are additionally required to alternate the positive and negative numbers in the output ordering. This problem also has several simple 2-approximations. We show that it can be approximated to within 1.79. Then we show that this problem is closely related to an optimization version of the gasoline puzzle due to Lov\'asz, in which we want to minimize the size of the gas tank necessary to go around the track. We present a 2-approximation for this problem, using a natural linear programming relaxation whose feasible solutions are doubly stochastic matrices. Our novel rounding algorithm is based on a transformation that yields another doubly stochastic matrix with special properties, from which we can extract a suitable permutation.