36. A derivative-free regularization algorithm for equality constrained nonlinear least squares problems
Invited abstract in session MB-35: Nonlinear Optimization Algorithms and Applications: 1 , stream Continuous and mixed-integer nonlinear programming: theory and algorithms.
Monday, 10:30-12:00Room: Michael Sadler LG15
Authors (first author is the speaker)
| 1. | Jinyan Fan
|
| Shanghai Jiao Tong University |
Abstract
In this talk, we present a derivative-free regularization algorithm for the equality constrained nonlinear least squares problem, where the Jacobian matrices of the objective function and constraints are unavailable or expensive to compute. We approximate the Jacobian matrices via orthogonal spherical smoothing. At each iteration, a regularized augmented Lagrangian subproblem is solved to obtain a Newton-like step. If a sufficient decrease in the merit function of the approximate KKT system is achieved, the step is accepted, otherwise a derivative-free LM algorithm is applied to get another step to satisfy the sufficient decrease condition. It is shown that the algorithm either finds an approximate KKT point with arbitrary high probability or converges to a stationary point of constraints violation almost surely.
Keywords
- Programming, Nonlinear
- Continuous Optimization
- Mathematical Programming
Status: accepted
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