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D-Index & Metrics

Mathematics

D-Index
39
Citations
16910
World Ranking
2116
National Ranking
894

Overview

Andrew R. Conn is affiliated with the IBM Thomas J. Watson Research Center in the United States. The scientist's research spans across multiple fields, including computer science and engineering, with significant contributions to aerospace engineering and artificial intelligence.

Their recent publications cover a range of topics in optimization and machine learning within both theoretical and applied domains. These works include:

  • An ADMM Based Framework for AutoML Pipeline Configuration, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Mixed-integer nonlinear and continuous optimization formulations for aircraft conflict avoidance via heading and speed deviations, 2023, European Journal of Operational Research
  • A derivative-free exact penalty algorithm: basic ideas, convergence theory and computational studies, 2022, Computational and Applied Mathematics
  • The continuous quadrant penalty formulation of logical constraints, 2023, Open Journal of Mathematical Optimization

Andrew R. Conn frequently collaborates with other researchers. Notable coauthors include:

  • Sonia Cafieri
  • Marcel Mongeau
  • Sijia Liu
  • Parikshit Ram
  • Deepak Vijaykeerthy

The scientist's work appears in several publication venues, including:

  • Proceedings of the AAAI Conference on Artificial Intelligence
  • European Journal of Operational Research
  • Computational and Applied Mathematics
  • Open Journal of Mathematical Optimization

Main fields of study encompass computer science and engineering, with subfields focusing on aerospace engineering, artificial intelligence, and computational theory and mathematics. Additional subfields include spectroscopy and general economics, econometrics, and finance.

Key topics addressed in their research are:

  • Air Traffic Management and Optimization
  • Machine Learning and Data Classification
  • Analytical Chemistry and Chromatography
  • Machine Learning and Algorithms
  • Aviation Industry Analysis and Trends
  • Aerospace and Aviation Technology
  • Advanced Optimization Algorithms Research

Best Publications

  • Trust Region Methods

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

  • Introduction to derivative-free optimization

    Andrew R. Conn;Katya Scheinberg;Luis N. Vicente

  • 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

  • An algorithmic framework for convex mixed integer nonlinear programs

    Pierre Bonami;Lorenz T. Biegler;Andrew R. Conn;GéRard CornuéJols

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

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

  • Global convergence of a class of trust region algorithms for optimization with simple bounds

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

  • 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

  • Recent progress in unconstrained nonlinear optimization without derivatives

    A. R. Conn;K. Scheinberg;Ph. L. Toint

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

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

  • Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points

    Andrew R. Conn;Katya Scheinberg;Luís N. Vicente

  • On the convergence of derivative-free methods for unconstrained optimization

    Andy Conn;Katya Scheinberg;Philippe Toint

  • An Efficient Method to Solve the Minimax Problem Directly

    C. Charalambous;A. R. Conn

  • An Efficient Primal-Dual Interior-Point Method for Minimizing a Sum of Euclidean Norms

    Knud D. Andersen;Edmund Christiansen;Andrew R. Conn;Michael L. Overton

  • Nonlinear programming via an exact penalty function: Asymptotic analysis

    Thomas F. Coleman;Andrew R. Conn

  • Geometry of interpolation sets in derivative free optimization

    A. R. Conn;K. Scheinberg;Luís N. Vicente

  • An Algorithm using Quadratic Interpolation for Unconstrained Derivative Free Optimization

    Andrew R. Conn;Philippe L. Toint

  • On the Local Convergence of a Quasi-Newton Method for the Nonlinear Programming Problem

    Thomas F. Coleman;Andrew R. Conn

  • A derivative free optimization algorithm in practice

    A. Conn;K. Scheinberg;Ph. Toint

  • Minimization Techniques for Piecewise Differentiable Functions: The l_1 Solution to an Overdetermined Linear System

    Unknown

  • Convergence Properties of an Augmented Lagrangian Algorithm for Optimization with a Combination of General Equality and Linear Constraints

    A. R. Conn;N. Gould;A. Sartenaer;Ph. L. Toint

Frequent Co-Authors

Nicholas I. M. Gould
Nicholas I. M. Gould University of Oxford
Katya Scheinberg
Katya Scheinberg Cornell University
Luís Nunes Vicente
Luís Nunes Vicente Lehigh University
Thomas F. Coleman
Thomas F. Coleman University of Waterloo
Philippe L. Toint
Philippe L. Toint University of Namur
Lorenz T. Biegler
Lorenz T. Biegler Carnegie Mellon University
Gérard Cornuéjols
Gérard Cornuéjols Carnegie Mellon University
Ignacio E. Grossmann
Ignacio E. Grossmann Carnegie Mellon University
Michael L. Overton
Michael L. Overton Courant Institute of Mathematical Sciences
Michael A. Saunders
Michael A. Saunders Stanford University

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