A Logarithmic Barrier Function Algorithm for Quadratically Constrained Convex Quadratic Programming
SIAM Journal on Optimization1991Vol. 1(2), pp. 252–267
Citations Over Time
Abstract
An interior point method for quadratically constrained convex quadratic programming is presented that is based on a logarithmic barrier function approach and terminates at a required accuracy of an approximate solution in polynomial time. This approach generates a sequence of unconstrained optimization problems, each of which is approximately solved by taking a single step in a Newton direction.
Related Papers
- → Representing quadratically constrained quadratic programs as generalized copositive programs(2012)61 cited
- → Semidefinite Programming Based Convex Relaxation for Nonconvex Quadratically Constrained Quadratic Programming(2019)3 cited
- Convex Relaxations with Second Order Cone Constraints for Nonconvex Quadratically Constrained Quadratic Programming(2016)
- → Semidefinite Relaxations for Mixed 0-1 Second-Order Cone Program(2012)1 cited
- An extended sequential quadratically constrained quadratic programming algorithm for nonlinear, semidefinite, and second-order cone programming(2013)