Richard Byrd

Professor Emeritus

Department of Computer Science

University of Colorado

Teaching Interests:

Numerical computatation, algorithms for constrained and unconstrained nonlinear optimization, linear programming, numerical linear algebra.

Research Interests:

Algorithms for constrained and unconstrained nonlinear optimization, nonlinear data fitting, optimization in machine learning, numerical linear algebra.

Current Projects:

Trust region methods for nonlinearly constrained optimization

Active set prediction

Limited memory methods for large-scale optimization

Analysis of quasi-Newton methods

Recent papers:

• "Inexact SQP Methods for Equality Constrained Optimization,"
by R.H. Byrd, F.E. Curtis and J. Nocedal
"A Preconditioned L-BFGS Algorithm with Application to Molecular Energy Minimization,"
by L. Jiang, R.H. Byrd, E. Eskow, and R.B. Schnabel, Technical Report CU-CS-982-04, Department of Computer Science, Nov. 2004.
"An Algorithm for Nonlinear Optimization Using Linear Programming and Equality Constrained Subproblems,"
by R. Byrd, N. Gould, J. Nocedal, and R. Waltz (May 2003).
"On the Convergence of Newton Iterations to Non-Stationary Points,"
(revised March 2002) by R. Byrd, M. Marazzi and J. Nocedal.
"A Large-Scale Stochastic-Perturbation Global Optimization Method for Molecular Cluster Problems"  (postscript), 
by R. Byrd, E. Eskow and R. Schnabel
"Predicting Protein Tertiary Structure Using a Global Optimization Algorithm with Smoothing"  (postscript), 
January 1999, by A. Azmi, R. Byrd, E. Eskow, R. Schnabel, S. Crivelli, T. Phillips and T. Head-Gordon.
"Retaining Convergence Properties of Trust Region Methods Without Extra Gradient Evaluations"
August 1998 (postscript), by H. Khalfan, R. Byrd, and R. Schnabel.
• "Global Optimization For Molecular Clusters Using A New Smoothing Approach"
(postscript), by C.-S. Shao, R. Byrd, E. Eskow, and R. Schnabel.
• "Towards a Discrete Newton Method with Memory for Large-scale
Optimization"(postscript), by R.H. Byrd, J. Nocedal and C. Zhu.
• "A Trust Region Method Based on Interior Point Techniques for Nonlinear Programming"
revised 1998, (postscript) by R.H. Byrd, J.C. Gilbert and J. Nocedal.
• "A Stochastic/Perturbation Global Optimization Algorithm for Distance Geometry
Problems"(postscript), by Z. Zou, R.H. Byrd, and R.B. Schnabel.
• "An Interior Point Algorithm for Large Scale Nonlinear Programming"
(postscript), by R.H. Byrd, M.E. Hribar and J. Nocedal.