A Safe First-Order Method for Pricing-Based Resource Allocation in Safety-Critical Networks

TL;DR

This paper presents SPNUM, a novel safe first-order algorithm for network utility maximization that guarantees safety constraints are met at every iteration while converging to optimal solutions in resource allocation problems.

Contribution

Introduces SPNUM, a safe first-order method that ensures feasibility at all steps and achieves sublinear regret in safety-critical network resource allocation.

Findings

Guarantees safety constraints at all iterations.

Achieves sublinear static regret of O(log T).

Effectively estimates user demand response to posted prices.

Abstract

We introduce a novel algorithm for solving network utility maximization (NUM) problems that arise in resource allocation schemes over networks with known safety-critical constraints, where the constraints form an arbitrary convex and compact feasible set. Inspired by applications where customers' demand can only be affected through posted prices and real-time two-way communication with customers is not available, we require an algorithm to generate ``safe prices''. This means that at no iteration should the realized demand in response to the posted prices violate the safety constraints of the network. Thus, in contrast to existing distributed first-order methods, our algorithm, called safe pricing for NUM (SPNUM), is guaranteed to produce feasible primal iterates at all iterations. At the heart of the algorithm lie two key steps that must go hand in hand to guarantee safety and convergence: 1) applying a projected gradient method on a shrunk feasible set to get the desired demand, and 2) estimating the price response function of the users and determining the price so that the induced demand is close to the desired demand. We ensure safety by adjusting the shrinkage to account for the error between the induced demand and the desired demand. In addition, by gradually reducing the amount of shrinkage and the step size of the gradient method, we prove that the primal iterates produced by the SPNUM achieve a sublinear static regret of ${\cal O}(\log{(T)})$ after $T$ time steps.