Abstract
In the last chapter, we saw that small modifications to the primal–dual interiorpoint algorithm allow it to be applied to quadratic programming problems as long as the quadratic objective function is convex. In this chapter, we shall go further and allow the objective function to be a general (smooth) convex function. In addition, we shall allow the feasible region to be any convex set given by a finite collection of convex inequalities.
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References
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Vanderbei, R. & Shanno, D. (1999), An Interior-Point Algorithm for Nonconvex Nonlinear Programming’, Computational Optimization and Applications 13, 231– 252.
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© 2008 Robert J.Vanderbei
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Vanderbei, R.J. (2008). Convex Programming. In: Linear Programming. International Series in Operations Research & Management Science, vol 114. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74388-2_25
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DOI: https://doi.org/10.1007/978-0-387-74388-2_25
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74387-5
Online ISBN: 978-0-387-74388-2
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