A Superlinearly Convergent Method for Minimization Problems with Linear Inequality Constraints.
Abstract
A method is described for minimizing a continuously differentiable function F(x) of n variables subject to linear inequality constraints. It can be applied under the same general assumptions as any method of feasible directions. If F(x) is twice continuously differentiable and the Hessian matrix of F(x) has certain properties, then the algorithm generates a sequence of points which converges superlinearly to the unique minimizer of F(x). No computation of second order derivatives is required. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1971
- Accession Number
- AD0729279
Entities
People
- K. Ritter
Organizations
- University of Wisconsin–Madison