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Nicholas I. M. Gould

Nicholas I. M. Gould

D-Index & Metrics

Mathematics

D-Index
58
Citations
19105
World Ranking
620
National Ranking
48

Engineering and Technology

D-Index
58
Citations
19199
World Ranking
2428
National Ranking
165

Research.com Recognitions

  • 2009 - SIAM Fellow For contributions to numerical continuous optimization.

Overview

Nicholas I. M. Gould is affiliated with the Rutherford Appleton Laboratory in the United Kingdom. Their research spans multiple fields, primarily focusing on Computer Science, Engineering, and Mathematics. Within these areas, Gould's work covers several subfields, including Numerical Analysis, Computational Mechanics, Computational Theory and Mathematics, Artificial Intelligence, and Control and Systems Engineering.

Their main topics of research include Advanced Optimization Algorithms Research, Sparse and Compressive Sensing Techniques, Stochastic Gradient Optimization Techniques, Optimization and Variational Analysis, Matrix Theory and Algorithms, Polynomial and Algebraic Computation, and Machine Learning and Algorithms.

Among Gould's selected recent publications are:

  • Strong Evaluation Complexity Bounds for Arbitrary-Order Optimization of Nonconvex Nonsmooth Composite Functions, 2020, arXiv (Cornell University)
  • Strong Evaluation Complexity Bounds for Arbitrary-Order Optimization of Nonconvex Nonsmooth Composite Functions, 2020, arXiv (Cornell University)
  • Approximating sparse Hessian matrices using large-scale linear least squares, 2023, Numerical Algorithms
  • Sharp Worst-Case Evaluation Complexity Bounds for Arbitrary-Order Nonconvex Optimization with Inexpensive Constraints, 2020, SIAM Journal on Optimization
  • Strong Evaluation Complexity of An Inexact Trust-Region Algorithm for Arbitrary-Order Unconstrained Nonconvex Optimization, 2020, arXiv (Cornell University)

Frequent publication venues where Gould's work appears include:

  • arXiv (Cornell University)
  • Numerical Algorithms
  • SIAM Journal on Optimization
  • The Journal of Open Source Software
  • Amicus Curiae

Gould has collaborated frequently with several co-authors. The most frequent collaborators are Philippe L. Toint, Coralia Cartis, Jaroslav Fowkes, J. A. Scott, and Olivia Liang.

Gould has authored a book published by the Society for Industrial and Applied Mathematics titled Evaluation Complexity of Algorithms for Nonconvex Optimization: Theory, Computation and Perspectives (2022).

Gould received recognition as a SIAM Fellow in 2009 for contributions to numerical continuous optimization.

Best Publications

  • Trust Region Methods

    Andrew R. Conn;Nicholas I. M. Gould;Philippe L. Toint

  • CUTE: constrained and unconstrained testing environment

    I. Bongartz;A. R. Conn;Nick Gould;Ph. L. Toint

  • A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

    Andrew R. Conn;Nicholas I. M. Gould;Philippe L. Toint

  • Lancelot: A FORTRAN Package for Large-Scale Nonlinear Optimization (Release A)

    A. R. Conn;N. I. M. Gould;Ph L. Toint

  • CUTEr and SifDec: A constrained and unconstrained testing environment, revisited

    Nicholas I. M. Gould;Dominique Orban;Philippe L. Toint

  • Global Convergence of a Trust-Region SQP-Filter Algorithm for General Nonlinear Programming

    Roger Fletcher;Nicholas I. M. Gould;Sven Leyffer;Philippe L. Toint

  • A globally convergent Lagrangian barrier algorithm for optimization with general inequality constraints and simple bounds

    A. R. Conn;Nick Gould;Ph. L. Toint

  • Constraint Preconditioning for Indefinite Linear Systems

    Carsten Keller;Nicholas I. M. Gould;Andrew J. Wathen

  • Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results

    Coralia Cartis;Nicholas I. Gould;Philippe L. Toint

  • CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization

    Nicholas I. Gould;Dominique Orban;Philippe L. Toint

  • Solving the Trust-Region Subproblem using the Lanczos Method

    Nicholas I. M. Gould;Stefano Lucidi;Massimo Roma;Philippe L. Toint

  • On the Solution of Equality Constrained Quadratic Programming Problems Arising in Optimization

    Nicholas I. M. Gould;Mary E. Hribar;Jorge Nocedal

  • Testing a class of methods for solving minimization problems with simple bounds on the variables

    Andrew R. Conn;Nicholas I. M. Gould;Philippe L. Toint

  • A numerical evaluation of sparse direct solvers for the solution of large sparse symmetric linear systems of equations

    Nicholas I. M. Gould;Jennifer A. Scott;Yifan Hu

  • Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function- and derivative-evaluation complexity

    Coralia Cartis;Nicholas I. M. Gould;Philippe L. Toint

  • Convergence of quasi-Newton matrices generated by the symmetric rank one update

    A. R. Conn;N. I. M. Gould;Ph. L. Toint

  • GALAHAD, a library of thread-safe Fortran 90 packages for large-scale nonlinear optimization

    Nicholas I. M. Gould;Dominique Orban;Philippe L. Toint

  • On the Complexity of Steepest Descent, Newton's and Regularized Newton's Methods for Nonconvex Unconstrained Optimization Problems

    C. Cartis;N. I. M. Gould;Ph. L. Toint

  • Numerical methods for large-scale nonlinear optimization

    Nick Gould;Dominique Orban;Philippe Toint

  • SIAM Journal on Optimization

    C Audet;H H Bauschke;L T Biegler;P L Combettes

  • SOLVING THE TRUST-REGION SUBPROBLEM USING THE

    Nicholas I. M. Gould;Stefano Lucidi;Massimo Roma;Philippe L. Toint

Frequent Co-Authors

Philippe L. Toint
Philippe L. Toint University of Namur
Andrew J. Wathen
Andrew J. Wathen University of Oxford
Sven Leyffer
Sven Leyffer Argonne National Laboratory
John Reid
John Reid Rutherford Appleton Laboratory
Iain S. Duff
Iain S. Duff Rutherford Appleton Laboratory
Jorge Nocedal
Jorge Nocedal Northwestern University
Christoph Ortner
Christoph Ortner University of Warwick
Michael A. Saunders
Michael A. Saunders Stanford University
Richard H. Byrd
Richard H. Byrd University of Colorado Boulder
Philip E. Gill
Philip E. Gill University of California, San Diego

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