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Iterative methods for large-scale unconstrained optimization


An unconstrained minimizer of a general nonlinear function may be found by solving a sequence of constrained subproblems in which a quadratic model function is minimized subject to a "trust-region" constraint on the norm of the change in variables. For the large-scale case, Steihaug has proposed an iterative method for the constrained subproblem based on the preconditioned conjugate-gradient (PCG) method. This method is terminated inside the trust region at an approximate minimizer or at the point where the iterates cross the trust-region boundary. When the iterates are terminated at the trust- region boundary, the final iterate is generally an inaccurate solution of the constrained subproblem. This may have an adverse affect on the efficiency and robustness of the overall trust-region method. A PCG-based method is proposed that may be used to solve the trust- region subproblem to any prescribed accuracy. The method starts by using a modified Steihaug method. If the solution lies on the trust-region boundary, a PCG-based sequential subspace minimization (SSM) method is used to solve the constrained problem over a sequence of evolving low-dimensional subspaces. A new regularized sequential Newton method is used to define basis vectors for the subspace minimization. Several preconditioners are proposed for the PCG iterations. Numerical results suggest that, in general, a trust-region method based on the proposed solver is more robust and requires fewer function evaluations than Steihaug's method

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