Name
Title | ||
|---|---|---|
A decomposition algorithm for unconstrained optimization problems with partial derivative information |
Abstract | ||
|---|---|---|
In this paper we consider the problem of minimizing a nonlinear function using partial derivative knowledge. Namely, the objective function is such that its derivatives with respect to a pre-specified block of variables cannot be computed. To solve the problem we propose a block decomposition method that takes advantage of both derivative-free and derivative-based iterations to account for the features of the objective function. Under standard assumptions, we manage to prove global convergence of the method to stationary points of the problem. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s11590-010-0270-2 | Optimization Letters |
Keywords | Field | DocType |
Unconstrained optimization, Block decomposition method, Derivative-free iteration | Convergence (routing),Mathematical optimization,Nonlinear system,Decomposition method (constraint satisfaction),Partial derivative,Stationary point,Optimization problem,Mathematics,Decomposition | Journal |
Volume | Issue | ISSN |
6 | 3 | 1862-4480 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||