Abstract
A convex optimization problem with linear equality constraints is solved by the unconstrained minimization of a sequence of convex quadratic functions. The idea of sequential quadratic programming is combined with the concept of regularized gap function to construct an exact differentiable penalty function. A descent algorithm is proposed along with some numerical illustrations.
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The authors thank the anonymous reviewers very much for their constructive and detailed feedback.
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Sadhu, R., Nahak, C., Dash, S.P. (2021). Unconstrained Reformulation of Sequential Quadratic Programming and Its Application in Convex Optimization. In: Laha, V., Maréchal, P., Mishra, S.K. (eds) Optimization, Variational Analysis and Applications. IFSOVAA 2020. Springer Proceedings in Mathematics & Statistics, vol 355. Springer, Singapore. https://doi.org/10.1007/978-981-16-1819-2_8
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