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arXiv · 1603.05117

An Algorithm for Solving Quadratic Optimization Problems with Nonlinear Equality Constraints

Tuan T. Nguyen·Mircea Lazar·Hans Butler
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Abstract

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.

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Tuan T. Nguyen, Mircea Lazar, Hans Butler. 2016-03-16. An Algorithm for Solving Quadratic Optimization Problems with Nonlinear Equality Constraints. https://arxiv.org/abs/1603.05117

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