Abstract
An algorithm for solving equality constrained optimization problems is proposed. It can deal with nonconvex functions and uses the truncated conjugate gradient algorithm for detecting nonconvexity. The algorithm ensures convergence from remote starting point by using line-search. Numerical experiments are reported, comparing the approach with the one implemented in the trust region codes ETR and Knitro.
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Chauvier, L., Fuduli, A., Gilbert, C.J. (2003). A truncated SQP algorithm for solving nonconvex equality constrained optimization problems. In: Di Pillo, G., Murli, A. (eds) High Performance Algorithms and Software for Nonlinear Optimization. Applied Optimization, vol 82. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0241-4_7
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DOI: https://doi.org/10.1007/978-1-4613-0241-4_7
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