Efficient descent direction of a primal-dual interior point algorithm for convex quadratic optimization
Abstract
We introduce a new interior-point method for solving convex quadratic programming under full-Newton step. The method involves an equivalent algebraic transformation applied to the system defining the central path. This approach provides an efficient search direction for the considered algorithm. Furthermore, we show that the introduced method produces an optimal solution within polynomial time. The established numerical tests conclude that the newly proposed algorithm is not only polynomial but requires a number of iterations clearly lower than that obtained theoretically.
Fields of Study & Subjects
Advanced Optimization Algorithms Research
Stochastic Gradient Optimization Techniques
Optimization and Variational Analysis
Publication details
- Journal / source
- Journal of Information and Optimization Sciences
- Volume
- 47
- Issue
- 1
- Pages
- 99–117
- Publication date
- 1 January 2026