An adaptive cubic regularisation algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity
Abstract: The adaptive cubic overestimation algorithm described in Cartis, Gould and Toint (2007) is adapted to the problem of minimizing a nonlinear, possibly nonconvex, smooth objective function over a convex domain. Convergence to first-order critical points is shown under standard assumptions, but without any Lipschitz continuity requirement on the objective's Hessian. A worst-case complexity analysis in terms of evaluations of the problem's function and derivatives is also presented for the Lipschitz continuous case and for a variant of the resulting algorithm. This analysis extends the best known bound for general unconstrained problems to nonlinear problems with convex constraints.
Keywords: Nonlinear optimization, convex constraints, cubic regularisation, numerical algorithms, global convergence, worst-case complexity.
Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )
Category 2: Nonlinear Optimization (Bound-constrained Optimization )
Citation: Technical Report 08/05R, Department of Mathematics, FUNDP-Unviversity of Namur, Namur, Belgium
Entry Submitted: 02/20/2009
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