Checking local optimality in constrained quadratic programming is NP-hard

Authors:
P. M. Pardalos;G. Schnitger
Affiliations:
Computer Science Department, The Pennsylvania State University, 333 Whitmore Library, University Park, PA 16802, USA;Computer Science Department, The Pennsylvania State University, 333 Whitmore Library, University Park, PA 16802, USA
Venue:
Operations Research Letters
Year:
1988

Citing 4
Cited 5

The complexity of analog computation

Mathematics and Computers in Simulation
Constrained global optimization: algorithms and applications

Constrained global optimization: algorithms and applications
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
The complexity of theorem-proving procedures

STOC '71 Proceedings of the third annual ACM symposium on Theory of computing

Solving a combined cutting-stock and lot-sizing problem with a column generating procedure

Computers and Operations Research
Global optimization over a box via canonical dual function

Journal of Computational and Applied Mathematics
A dynamic convexized method for nonconvex mixed integer nonlinear programming

Computers and Operations Research
Complexity of uniqueness and local search in quadratic 0-1 programming

Operations Research Letters
Minimizing ordered weighted averaging of rational functions with applications to continuous location

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we prove that the problem of checking local optimality for a feasible point and the problem of checking if a local minimum is strict, are NP-hard problems. As a consequence the problem of checking whether a function is locally strictly convex is also NP-hard.