Nonmonotone Filter Method for Nonlinear Optimization
Abstract: We propose a new nonmonotone filter method to promote global and fast local convergence for sequential quadratic programming algorithms. Our method uses two filters: a global g-filter for global convergence, and a local nonmonotone l-filter that allows us to establish fast local convergence. We show how to switch between the two filters efficiently, and we prove global and superlinear local convergence. A special feature of the proposed method is that it does not require second-order correction steps. We present preliminary numerical results comparing our implementation with a classical filter SQP method.
Keywords: Nonlinear optimization, nonmonotone filter, global convergence, local convergence
Category 1: Nonlinear Optimization
Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )
Citation: Preprint ANL/MCS-P1679-0909, Argonne National Laboratory, Mathematics and Computer Science Division, September 2009.
Entry Submitted: 10/27/2009
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