Solving optimization problems with diseconomies of scale via decoupling

We present a new framework for solving optimization problems with a diseconomy of scale. In such problems, our goal is to minimize the cost of resources used to perform a certain task. The cost of resources grows superlinearly, as x^q, q ≥ 1, with the amount x of resources used. We define a novel linear programming relaxation for such problems and then show that the integrality gap of the relaxation is A_q, where A_q is the q-th moment of the Poisson random variable with parameter 1. Using our framework, we obtain approximation algorithms for the Minimum Energy Efficient Routing, Minimum Degree Balanced Spanning Tree, Load Balancing on Unrelated Parallel Machines, and Unrelated Parallel Machine Scheduling with Nonlinear Functions of Completion Times problems. Our analysis relies on the decoupling inequality for nonnegative random variables. The inequality states that (equation Presented) , where X_i are independent nonnegative random variables, Y_i are possibly dependent nonnegative random variables, and each Y_i has the same distribution as X_i. The inequality was proved by de la Peña in 1990. De la Peña, Ibragimov, and Sharakhmetov showed that C_q ≤ 2 for q ∈ (1, 2) and C_q ≤ A¹ _q ^/q for q ≥ 2. We show that the optimal constant is C_q = A¹ _q ^/q for any q ≥ 1. We then prove a more general inequality: For every convex function φ, (equation Presented) , and, for every concave function ψ, (equation Presented) , where P is a Poisson random variable with parameter 1 independent of the random variables Y_i