Faculty Directory
Andreas Waechter

Professor of Industrial Engineering & Management Sciences

Contact

2145 Sheridan Road
Tech E280
Evanston, IL 60208-3109

Email Andreas Waechter

Website

Andreas Waechter's Homepage

Departments

Industrial Engineering and Management Sciences

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Education

Ph.D., Chemical Engineering, Carnegie Mellon University, Pittsburgh,PA

M.S. Mathematics, University of Cologne, Germany


Research Interests

Large-scale nonlinear continuous optimization; mixed-integer nonlinear optimization; open source software implementation; application of optimization algorithms to industrial and scientific problems


Selected Publications

  • N. Keskar, Andreas Waechter, “A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization”, Optimization Methods and Software, (2019)
  • M. Ben Feng, Alvaro Maggiar, Jeremy Staum, Andreas Waechter, “Uniform convergence of sample average approximation with adaptive multiple importance sampling”, WSC 2018 - 2018 Winter Simulation Conference, (2019)
  • Mingbin Feng, John J. Mitchell, Jong Shi Pang, Xin Shen, Andreas Waechter, “Complementarity Formulations of ?0-norm Optimization”, Pacific Journal of Optimization, (2018)
  • Mark Semelhago, Barry L Nelson, Andreas Waechter, Eunhye Song, “Computational methods for optimization via simulation using Gaussian Markov Random Fields”, 2017 Winter Simulation Conference, WSC 2017, (2018)
  • Hanyu Gao, Andreas Waechter, Ivan A. Konstantinov, Steven G. Arturo, Linda J Broadbelt, “Application and comparison of derivative-free optimization algorithms to control and optimize free radical polymerization simulated using the kinetic Monte Carlo method”, Computers and Chemical Engineering, (2018)
  • Francisco Jara-Moroni, Jong Shi Pang, Andreas Waechter, “A study of the difference-of-convex approach for solving linear programs with complementarity constraints”, Mathematical Programming, (2018)
  • Alvaro Maggiar, Andreas Waechter, Irina S. Dolinskaya, Jeremy Staum, “A derivative-free trust-region algorithm for the optimization of functions smoothed via Gaussian convolution using adaptive multiple importance sampling? ”, SIAM Journal on Optimization, (2018)
  • Alvaro Maggiar, Andreas Waechter, Irina S Dolinskaya, Jeremy C Staum, “A Derivative-Free Trust-Region Algorithm for the Optimization of Functions Smoothed via Gaussian Convolution Using Adaptive Multiple Importance Sampling”, , (2017)