Structured secant updates for nonlinear constrained optimization

[摘要] Two new updates are presented, the UHU update and a modified Gurwitz update, for approximating the Hessian of the Lagrangian in nonlinear constrained optimization problems. Under the standard assumptions, the new UHU algorithm is shown to converge locally at a two-step q-superlinear rate. With the additional assumption that the update can be performed at every iteration, the UHU method converges locally at a one-step q-superlinear rate. Numerical experiments are performed on some full Hessian methods including Powell;;s modified BFGS and Tapia;;s ASSA and SALSA algorithms, and on reduced Hessian methods including the two new updates, the Coleman-Fenyes update, the Nocedal-Overton method, and the two-stage Gurwitz update. These experiments show that the new updates compare favorably with existing methods.

[发布机构] Rice University

[效力级别] Operations research