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Abstract : |
Abstract. A new method, the sequential subspace method (SSM), is developed for the problem of minimizing a quadratic over a sphere. In our scheme, the quadratic is minimized over a subspace which is adjusted in successive iterations to ensure convergence to an optimum. When a sequential quadratic programming iterate is included in the subspace, convergence is locally quadratic. Numerical comparisons with other recent methods are given. Key words. Trust region subproblem, large-scale optimization, sparse optimization, quadratic optimization, quadratic programming, minimal residual, preconditioning, Krylov space, Arnoldi orthogonalization, successive overrelaxation, Gauss-Seidel., |
