2016/03/16 by Tuan Thanh Nguyen, Mircea Lazar, Nguyen, Tuan T. +3
Computer Science · Engineering · Mathematics · #Advanced Optimization Algorithms Research #Control Systems and Identification #FOS: Mathematics #Metaheuristic Optimization Algorithms Research #Numerical Analysis (math.NA) #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.1603.05117
openalex publication_date 2016/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The classical method to solve a quadratic optimization problem with nonlinear equality constraints is to solve the Karush-Kuhn-Tucker (KKT) optimality conditions using Newton's method. This approach however is usually computationally demanding, especially for large-scale problems. This paper presents a new computationally efficient algorithm for solving quadratic optimization problems with nonlinear equality constraints. It is proven that the proposed algorithm converges locally to a solution of the KKT optimality conditions. Two relevant application problems, fitting of ellipses and state reference generation for electrical machines, are presented to demonstrate the effectiveness of the proposed algorithm.