A linear-time algorithm for generalized trust region subproblems

TL;DR

This paper introduces the first provably linear-time algorithm for approximately solving the generalized trust region subproblem, which involves minimizing a quadratic function over a quadratic constraint, under certain regular conditions.

Contribution

The paper extends a recent linear-time algorithm for the trust region subproblem to the generalized case, demonstrating that the optimal solution lies in a compact convex set and enabling linear-time computation.

Findings

Algorithm operates in linear time relative to input size.

Optimal solutions are contained within a compact convex set.

Bounds on the optimal value can be computed efficiently.

Abstract

In this paper, we provide the first provable linear-time (in the number of non-zero entries of the input) algorithm for approximately solving the generalized trust region subproblem (GTRS) of minimizing a quadratic function over a quadratic constraint under some regular conditions. Our algorithm is motivated by and extends a recent linear-time algorithm for the trust region subproblem by Hazan and Koren [Math. Program., 2016, 158(1-2): 363-381]. However, due to the non-convexity and non-compactness of the feasible region, such an extension is nontrivial. Our main contribution is to demonstrate that under some regular condition, the optimal solution is in a compact and convex set and lower and upper bounds of the optimal value can be computed in linear time. Using these properties, we develop a linear-time algorithm for the GTRS.