Results 281 to 290 of about 184,637 (335)

Unconstrained quadratic bivalent programming problem

European Journal of Operational Research, 1984
The problem of minimising an unconstrained quadratic function of 0-1 variables is considered in this paper. A local minimising point is defined; and necessary and sufficient conditions for such a point are identified. A branching and pruning algorithm, which essentially generates all the local minimising points, is proposed to solve the problem ...
Gulati, V. P., Gupta, S. K., Mittal, A. K.   +2 more
openaire   +1 more source

Constraint exploration method for quadratic programming problem

Applied Mathematics and Computation, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mohd, Ismail, Dasril, Yosza
openaire   +2 more sources

On the indefinite quadratic bilevel programming problem

OPSEARCH, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alemayehu, Getinet, Narang, Ritu, Arora, S. R.   +2 more
openaire   +1 more source

Quadratic Programming Problems

1999
In this chapter nonconvex quadratic programming test problems are considered. These test problems have a quadratic objective function and linear constraints. Quadratic programming has numerous applications (Pardalos and Rosen (1987), Floudas and Visweswaran (1995)) and plays an important role in many nonlinear programming methods.
Christodoulos A. Floudas, Pãnos M. Pardalos, Claire S. Adjiman, William R. Esposito, Zeynep H. Gümüş, Stephen T. Harding, John L. Klepeis, Clifford A. Meyer, Carl A. Schweiger   +8 more
openaire   +1 more source

Sequential Quadratic Programming for Parameter Identification Problems

IFAC Proceedings Volumes, 1989
Abstract Sequential quadratic programming (SQP) is a technique for nonlinear equality constrained minimization problems, which, from the point of view of local convergence, is equivalent to finding a root of the gradient of the Lagrangian by Newton's method, if the second order sufficient conditions hold. For general, unstructured, finite dimensional
D.M. Hwang, C.T. Kelley
openaire   +1 more source

The Indefinite Quadratic Programming Problem

Operations Research, 1979
We develop several algorithms that obtain the global optimum to the indefinite quadratic programming problem. A generalized Benders cut method is employed. These algorithms all possess ϵ-finite convergence. To obtain finite convergence, we develop exact cuts, which are locally precise representations of a reduced objective.
openaire   +1 more source

Reducing quadratic programming problem to regression problem: Stepwise algorithm

European Journal of Operational Research, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Dong Q., Chukova, Stefanka, Lai, C. D.   +2 more
openaire   +1 more source

On Copositive Programming and Standard Quadratic Optimization Problems

Journal of Global Optimization, 2000
The authors consider quadratic optimization problems of the form \[ x^TAx\to \text{maximum subject to }x\in\Delta \] where \(A\) is an arbitrary symmetric \(n\times n\) matrix and \(\Delta\) is the standard simpiex in the \(n\)-dimensional Euclidean space \(\mathbb{R}^n\), \(\Delta= \{x\in R^n_+: e^Tx=1\}\).
Bomze, I.M., Dür, M., Klerk, E. de, Roos, C., Quist, A.J., Terlaky, T.   +5 more
openaire   +3 more sources

Solving quadratic assignment problems using convex quadratic programming relaxations

Optimization Methods and Software, 2001
We describe a branch-and-bound algorithm for the quadratic assignment problem (QAP) that uses a convex quadratic programming (QP) relaxation to obtain a bound at each node. The QP subproblems are approximately solved using the Frank-Wolfe algorithm, which in this case requires the solution of a linear assignment problem on each iteration. Our branching
Kurt M. Anstreicher, Nathan W. Brixius
openaire   +1 more source

Finding Symmetry Groups of Some Quadratic Programming Problems

Numerical Mathematics: Theory, Methods and Applications, 2023
Summary: Solution and analysis of mathematical programming problems may be simplified when these problems are symmetric under appropriate linear transformations. In particular, a knowledge of the symmetries may help decrease the problem dimension, reduce the size of the search space by means of linear cuts.
Eremeev, Anton V., Yurkov, Alexander S.
openaire   +2 more sources