Some NP-complete problems in quadratic and nonlinear programming
Mathematical Programming: Series A and B
A new algorithm for solving the general quadratic programming problem
Computational Optimization and Applications
On generalized bisection of n-simplices
Mathematics of Computation
A mathematical view of interior-point methods in convex optimization
A mathematical view of interior-point methods in convex optimization
A Complementary Pivoting Approach to the Maximum Weight Clique Problem
SIAM Journal on Optimization
Solving a Class of Linearly Constrained Indefinite QuadraticProblems by D.C. Algorithms
Journal of Global Optimization
On Standard Quadratic Optimization Problems
Journal of Global Optimization
A Simplicial Branch-and-Bound Method for Solving Nonconvex All-Quadratic Programs
Journal of Global Optimization
A New Semidefinite Programming Bound for Indefinite Quadratic Forms Over a Simplex
Journal of Global Optimization
On Copositive Programming and Standard Quadratic Optimization Problems
Journal of Global Optimization
On solving the maximum clique problem
Journal of Global Optimization
Annealed replication: a new heuristic for the maximum clique problem
Discrete Applied Mathematics
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Computational Optimization and Applications
D.C. Versus Copositive Bounds for Standard QP
Journal of Global Optimization
A bilinear formulation for vector sparsity optimization
Signal Processing
Journal of Global Optimization
Journal of Global Optimization
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This paper explores several possibilities for applying branch-and-bound techniques to a central problem class in quadratic programming, the so-called Standard Quadratic Problems (StQPs), which consist of finding a (global) minimizer of a quadratic form over the standard simplex. Since a crucial part of the procedures is based on efficient local optimization, different procedures to obtain local solutions are discussed, and a new class of ascent directions is proposed, for which a convergence result is established. Main emphasis is laid upon a d.c.-based branch-and-bound algorithm, and various strategies for obtaining an efficient d.c. decomposition are discussed.