J
John E. Dennis
Researcher at Rice University
Publications - 150
Citations - 31624
John E. Dennis is an academic researcher from Rice University. The author has contributed to research in topics: Nonlinear programming & Constrained optimization. The author has an hindex of 55, co-authored 150 publications receiving 30156 citations. Previous affiliations of John E. Dennis include University of Houston & Cornell University.
Papers
More filters
Book
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
TL;DR: In this paper, Schnabel proposed a modular system of algorithms for unconstrained minimization and nonlinear equations, based on Newton's method for solving one equation in one unknown convergence of sequences of real numbers.
Book
Numerical methods for unconstrained optimization and nonlinear equations
TL;DR: Newton's Method for Nonlinear Equations and Unconstrained Minimization and methods for solving nonlinear least-squares problems with Special Structure.
Journal ArticleDOI
Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems
Indraneel Das,John E. Dennis +1 more
TL;DR: In this paper, an alternate method for finding several Pareto optimal points for a general nonlinear multicriteria optimization problem is proposed, which can handle more than two objectives while retaining the computational efficiency of continuation-type algorithms.
Journal ArticleDOI
Quasi-Newton Methods, Motivation and Theory
John E. Dennis,Jorge J. Moré +1 more
TL;DR: In this paper, an attempt to motivate and justify quasi-Newton methods as useful modifications of Newton''s method for general and gradient nonlinear systems of equations is made, and references are given to ample numerical justification; here we give an overview of many of the important theoretical results.
Journal ArticleDOI
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
Charles Audet,John E. Dennis +1 more
TL;DR: The main result of this paper is that the general MADS framework is flexible enough to allow the generation of an asymptotically dense set of refining directions along which the Clarke derivatives are nonnegative.