In this paper the quasilinearization technique along with the Chebyshev polynomials of the first type are used to solve the nonlinear-quadratic optimal control problem with terminal state constraints. The quasilinearization is used to convert the nonlinear quadratic optimal control problem into sequence of linear quadratic optimal control problems. Then by approximating the state and control va...

In the paper we prove that any nonconvex quadratic problem over some set K ⊆Rn with additional linear and binary constraints can be rewritten as linear problem over the cone, dual to the cone of K-semidefinite matrices. We show that when K is defined by one quadratic constraint or by one concave quadratic constraint and one linear inequality, then the resulting K-semidefinite problem is actuall...

This paper presents fuzzy goal programming approach for solving multiobjective quadratic programming problem. The problem deals with a decisionmaking unit with multiple objective functions, which are quadratic in nature and the system constraints are linear functions. In the proposed approach, we first formulate the quadratic membership functions by determining best solution of the objective fu...

In this paper, we consider partial Lagrangian relaxations of continuous quadratic formulations of the Quadratic Assignment Problem (QAP) where the assignment constraints are not relaxed. These relaxations are a theoretical limit for semidefinite relaxations of the QAP using any linearized quadratic equalities made from the assignment constraints. Using this framework, we survey and compare stan...

We show that recently developed interior point methods for quadratic programming and linear complementarity problems can be put to use in solving discrete-time optimal control problems, with general pointwise constraints on states and controls. We describe interior point algorithms for a discrete time linear-quadratic regulator problem with mixed state/control constraints, and show how it can b...

The paper deals with a method for solving general convex quadratic programming problems with equality and inequality constraints. The interest in such problems comes from at least two facts. First, quadratic models are widely used in real-life applications. Second, in many algorithms for nonlinear programming, a search direction is determined at each iteration as a solution of a quadratic probl...

We give a complete characterization of constant quadratic functions over an affine variety. This result is used to convexify the objective function of a general quadratic programming problem (Pb) which contains linear equality constraints. Thanks to this convexification, we show that one can express as a semidefinite program the dual of the partial Lagrangian relaxation of (Pb) where the linear...

Recently, Liu and Wang described an interesting numerical method to a special class of interval quadratic programming, where the linear term in objective function and constraints involving interval coefficients (Appl. Math. Comput. (2007), doi:10.1016/j.amc.2006.12.007). In this paper, we generalize Liu and Wang’s method to general interval quadratic programming, where all coefficients in the o...