In a single-objective setting, nonconvex quadratic problems can equivalently be reformulated as convex problems over the cone of completely positive matrices. In small dimensions this cone equals the cone of matrices which are entrywise nonnegative and positive semidefinite, so the convex...

We develop and implement a portfolio optimization method for building investment portfolios that dominate a given benchmark index in terms of third-degree stochastic dominance. Our approach relies on the properties of the semi-variance function, a refinement of an existing 'super-convex'...

This paper reviews various optimization approaches used to address a variety of issues related to risk in agricultural finance and farm management. The central focus is in the Markowitz mean‐variance model, which represents the classical approach to balancing risk and returns in an...

A quadratic programming model is developed for predicting the credit card holders' behaviors. This quadratic program is solved by a new algorithm. This algorithm optimizes the objective function along certain directions in the null space of the constraint equations. In this work, the results...

Four NP-hard optimization problems on graphs are studied: The vertex separator problem, the edge separator problem, the maximum clique problem, and the maximum independent set problem. We show that the vertex separator problem is equivalent to a continuous bilinear quadratic program. This...

In this paper, an energy management strategy is proposed for a series plug-in hybrid electric vehicle. A number of quadratic equations are employed to determine the engine fuel-rate with respect to battery power. The problem is solved by using quadratic programming and simulated annealing method...

The bipartite boolean quadratic programming problem (BBQP) is a generalization of the well studied boolean quadratic programming problem. The model has a variety of real life applications; however, empirical studies of the model are not available in the literature, except in a few isolated...

The Standard Quadratic Problem (StQP) is an NP-hard problem with many local minimizers (stationary points). In the literature, heuristics based on unconstrained continuous non-convex formulations have been proposed (Bomze & Palagi, 2005; Bomze, Grippo, & Palagi, 2012) but none dominates the other in...

In this paper we study a class of quadratic maximization problems and their semidefinite programming (SDP) relaxation. For a special subclass of the problems we show that the SDP relaxation provides an exact optimal solution. Another subclass, which is ${\\cal NP}$-hard, guarantees that the SDP...