On the complexity of finding a local minimizer of a quadratic function over a polytope
This addresses a fundamental complexity problem in numerical optimization, with implications for algorithm design in fields like operations research and machine learning, though it is incremental in the sense of providing a negative result rather than a new method.
The paper tackles the problem of finding a local minimizer of a quadratic function over a polytope, showing that unless P=NP, no polynomial-time algorithm can find a point within Euclidean distance c^n of such a minimizer, answering a long-standing open question from 1992.
We show that unless P=NP, there cannot be a polynomial-time algorithm that finds a point within Euclidean distance $c^n$ (for any constant $c \ge 0$) of a local minimizer of an $n$-variate quadratic function over a polytope. This result (even with $c=0$) answers a question of Pardalos and Vavasis that appeared in 1992 on a list of seven open problems in complexity theory for numerical optimization. Our proof technique also implies that the problem of deciding whether a quadratic function has a local minimizer over an (unbounded) polyhedron, and that of deciding if a quartic polynomial has a local minimizer are NP-hard.