Why Is John E. Dennis Influential?
According to Wikipedia John Emory Dennis, Jr. , is an American mathematician who has made major contributions in mathematical optimization. Dennis is currently a Noah Harding professor emeritus and research professor in the department of computational and applied mathematics at Rice University in Houston, Texas. His research interests include optimization in engineering design. He is the founder and editor-in-chief of the SIAM Journal on Optimization. In 2010, he was elected a Fellow of the Society for Industrial and Applied Mathematics.
John E. Dennis's Published Works Numerical methods for unconstrained optimization and nonlinear equations Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems Quasi-Newton Methods, Motivation and Theory A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems Mesh Adaptive Direct Search Algorithms for Constrained Optimization A rigorous framework for optimization of expensive functions by surrogates Analysis of Generalized Pattern Searches An Adaptive Nonlinear Least-Squares Algorithm Problem Formulation for Multidisciplinary Optimization A trust-region framework for managing the use of approximation models in optimization A Characterization of Superlinear Convergence and its Application to Quasi-Newton Methods On the Local and Superlinear Convergence of Quasi-Newton Methods Algorithm 573: NL2SOL—An Adaptive Nonlinear Least-Squares Algorithm [E4] Derivative free analogues of the Levenberg-Marquardt and Gauss algorithms for nonlinear least squares approximation A Pattern Search Filter Method for Nonlinear Programming without Derivatives Direct Search Methods on Parallel Machines NORMAL-BOUNDARY INTERSECTION: AN ALTERNATE METHOD FOR GENERATING PARETO OPTIMAL POINTS IN MULTICRITERIA OPTIMIZATION PROBLEMS OrthoMADS: A Deterministic MADS Instance with Orthogonal Directions Two new unconstrained optimization algorithms which use function and gradient values Least Change Secant Updates for Quasi-Newton Methods A trust region strategy for nonlinear equality constrained op-timization Pattern Search Algorithms for Mixed Variable Programming A Progressive Barrier for Derivative-Free Nonlinear Programming Multidirectional search: a direct search algorithm for parallel machines Optimal Aeroacoustic Shape Design Using the Surrogate Management Framework Direct Search Methods on Parallel Machines Trust-Region Interior-Point SQP Algorithms for a Class of Nonlinear Programming Problems Toward a Unified Convergence Theory for Newton-Like Methods The Algebraic Theory of Matrix Polynomials A Global Convergence Theory for General Trust-Region-Based Algorithms for Equality Constrained Optimization Convergence Theorems for Least-Change Secant Update Methods, Optimization on Microcomputers: The Nelder-Mead Simplex Algorithm Optimization Using Surrogate Objectives on a Helicopter Test Example Pattern search algorithms for mixed variable general constrained optimization problems Trailing-edge noise reduction using derivative-free optimization and large-eddy simulation MANAGING APPROXIMATION MODELS IN OPTIMIZATION Sizing and least-change secant methods Techniques for nonlinear least squares and robust regression A view of unconstrained optimization Comparison of derivative-free optimization methods for groundwater supply and hydraulic capture community problems On the Superlinear and Quadratic Convergence of Primal-Dual Interior Point Linear Programming Algorithms Globalization strategies for Mesh Adaptive Direct Search A Trust-Region Approach to Nonlinear Systems of Equalities and Inequalities Mixed Variable Optimization of the Number and Composition of Heat Intercepts in a Thermal Insulation System Convergence theory for the structured BFGS secant method with an application to nonlinear least squares Generalized pattern searches with derivative information A framework for managing models in nonlinear optimization of computationally expensive functions On Alternative Problem Formulations for Multidisciplinary Design Optimization Algorithms for solvents of matrix polynomials Parallel Space Decomposition of the Mesh Adaptive Direct Search Algorithm 10. Nonlinear Least Squares Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems Using simplex gradients of nonsmooth functions in direct search methods On the Convergence Theory of Trust-Region-Based Algorithms for Equality-Constrained Optimization On the Convergence of Broyden's Method for Nonlinear Systems of Equations A unified approach to global convergence of trust region methods for nonsmooth optimization SOME COMPUTATIONAL TECHNIQUES FOR THE NONLINEAR LEAST SQUARES PROBLEM A stability analysis for perturbed nonlinear iterative methods Managing surrogate objectives to optimize a helicopter rotor design - Further experiments Trust-Region Interior-Point Algorithms for Minimization Problems with Simple Bounds Generalized conjugate directions Inaccuracy in quasi-Newton methods: Local improvement theorems Suppression of vortex-shedding noise via derivative-free shape optimization Erratum: Mesh Adaptive Direct Search Algorithms for Constrained Optimization A global convergence theory for a class of trust region algorithms for constrained optimization A NEW DERIVATION OF SYMMETRIC POSITIVE DEFINITE SECANT UPDATES Direct secant updates of matrix factorizations A variable-metric variant of the Karmarkar algorithm for linear programming A Trust Region Strategy for Equality Constrained Optimization Direct Search Methods for Nonlinearly Constrained Optimization Using Filters and Frames A user's guide to nonlinear optimization algorithms Triangular Decomposition Methods for Solving Reducible Nonlinear Systems of Equations A robust trust region algorithm for nonlinear programming An Unconstrained Optimization Algorithm Which Uses Function and Gradient Values Chapter I A view of unconstrained optimization On the Matrix Polynomial, Lambda-Matrix and Block Eigenvalue Problems Multilevel algorithms for nonlinear optimization A New Algorithm for Nonlinear Least Squares Curve Fitting Inverse, Shifted Inverse, and Rayleigh Quotient Iteration as Newton's Method Approximation model management for optimization ALGORITHMS FOR BILEVEL OPTIMIZATION Pattern search in the presence of degenerate linear constraints On Some Methods Based on Broyden's Secant Approximation to the Hessian Problem formulations for systems of systems On the Second Order Convergence of Brown's Derivative-Free Method for Solving Simultaneous Nonlinear Equations. Trade-off studies in blackbox optimization A Curvilinear Search Using Tridiagonal Secant Updates for Unconstrained Optimization Nonlinear Programming by Mesh Adaptive Direct Searches Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization Parallel Implementations Of The Nelder-Mead Simplex Algorithm For Unconstrained Optimization Least-Change Sparse Secant Update Methods with Inaccurate Secant Conditions A new type of Chebyshev quadrature A global convergence theory for a general class of trust region algorithms for equality constrained optimization An efficient class of direct search surrogate methods for solving expensive optimization problems with CPU-time-related functions A New Nonlinear Equations Test Problem Surrogate Modelling and Space Mapping for Engineering Optimization: A Summary of the Danish Technical University November 2000 Workshop On Newton-like iteration functions: General convergence theorems and a specific algorithm Optimization of Hollow-Fiber Design and Low-Pressure Membrane System Operation A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems MoVars: Multidisciplinary Optimization Via Adaptive Response Surfaces Constrained Aeroacoustic Shape Optimization Using the Surrogate Management Framework Parallel Block Triangular Decompositions for Solving Sparse Nonlinear Systems of Equations Local Convergence Theorems for Quasi-Newton Methods A hybrid algorithm for solving sparse nonlinear systems of equations Comparing problem formulations for coupled sets of components Optimization and geophysical inverse problems Erratum: Convergence Theorems for Least-Change Secant Update Methods Parallel continuous optimization Algorithms for nonlinear problems which use discrete approximations to derivatives Pattern search in the presence of degeneracy 9. Secant Methods for Unconstrained Minimization Nonlinear Parameter Optimization On the Convergence of Mixed Integer Pattern Search Algorithms on the Convergence of Mixed Integer Pattern Search Algorithms Toward Direct Sparse Updates of Cholesky Factors Supplementary terminology for nonlinear iterative methods Some Minimal Properties of the Trapezoidal Rule Derivative-free optimization methods for surface structure determination of nanosystems Pattern search methods for linearly constrained minimization in the presence of degeneracy 3. Numerical Linear Algebra Background Pattern Search Methods in the Presence of Degeneracy Solving Computationally Expensive Optimization Problems with CPU Time-Correlated Functions IC S MULTILEVEL ALGORITHMS FOR NONLINEAR OPTIMIZATION Optimization Tools for Engineering Design Using Surrogate Functions Curve tailoring with interactive computer An Experimental Computer Network to Support Numerical Computation A Continuous Analogue Analysis of Nonlinear Iterative Methods Research on trust-region algorithms for nonlinear programming A user's guide to nonlinear optimization algorithms Pattern Search for Mixed Variable Optimization Problems A new parallel optimization algorithm for parameter identification in ordinary differential equations Numerical Optimization At the Center for Research On Parallel Computation The Combined Schubert/Secant Finite-Difference Algorithm for Solving Sparse Nonlinear Systems of Equations 5. Newton's Method for Nonlinear Equations and Unconstrained Minimization Characteristic Shape Sequences for Measures on Images Sensitivity to Constraints in Blackbox Optimization 8. Secant Methods for Systems of Nonlinear Equations 6. Globally Convergent Modifications of Newton's Method Comparing Problem Formulation for Coupled Sets of Component Solving Nonlinear Integer Programs with a Subgradient Approach on Parallel Computers SIAG/OPT Views-and-News pdfsubject Variable Metric Secant Updates from Matrix Factorizations. 7. Stopping, Scaling, and Testing Parallel Implementations of an Oil Refining Simulation Recent Advances in Optimization (July 24-26th, 2013) Integrated Approaches to Parallelism in Optimization and the Solution of Inverse Problems Mixed variable optimization of thenumber and composition of heat interceptsin a thermal insulation systemMichael Kokkolaras Some Issues in Nonlinear Programming Algorithms for Problems with Simulation Constraints. Cost Modeling and Optimization Considerations for Low Pressure Membrane Filtration Systems Research on trust-region algorithms for nonlinear programming. Final technical report, 1 January 1990--31 December 1992 11. Methods for Problems with Special Structure New Meta Algorithms for Engineering Design Using Surrogate Functions Research on trust-region algorithms for nonlinear programming. Progress report, January 1, 1991--December 31, 1991 Parallel Structured Optimization Algorithms for Inverse Problems Quasi-Newton methods for large scale nonlinear equations and constrained optimization: Progress report, July 1, 1987-June 30, 1988 On the Local Convergence of Nonlinear Successive Overrelaxation and Related Methods Derivative-free algorithms for unconstrained optimization problems Research in Constrained Optimization A Computational Note on Markov Decision Processes Without Discounting A Class of General Trust-region Multilevel Algorithms for Nonlinear Constrained Optimization: Global Convergence Analysis a Class of General Trust-region Multilevel Algorithms for Nonlinear Constrained Optimization: Global Convergence Analysis Managing the Choice of Surrogate Variables and the Use of Approximation Models to Optimize Expensive Functions Editorial — Special Issue on Surrogate Optimization Industrial Strength Derivative-Free Optimization Iterative Methods for Large Linear and Nonlinear Least Squares Problems. A Rigorous Framework for Optimizationof Expensive Functions by SurrogatesAndrew Optimization Using SurrogateObjectives On a Helicopter 2. Nonlinear Problems in One Variable Algorithms for Blackbox Optimization using Surrogate Function Optimization and Geophysical Inverse Problems October 2000 Nominations for 1997 elections A Rigorous Framework forOptimization of ExpensiveFunctions by SurrogatesAndrew 4. Multivariable Calculus Background More Papers This paper list is powered by the following services:
Other Resources About John E. Dennis What Schools Are Affiliated With John E. Dennis? John E. Dennis is affiliated with the following schools:
John E. Dennis's AcademicInfluence.com Rankings
