Faculty DirectoryAndreas Waechter
Professor of Industrial Engineering & Management Sciences
Contact
2145 Sheridan RoadTech D235
Evanston, IL 60208-3109
Email Andreas Waechter
Website
Departments
Industrial Engineering and Management Sciences
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
- Tu, Shenyinying; Wachter, Andreas; Wei, Ermin, A Two-Stage Decomposition Approach for AC Optimal Power Flow, IEEE Transactions on Power Systems 36(1):303-312 (2021).
- Pena-Ordieres, Alejandra; Molzahn, Daniel K.; Roald, Line A.; Wachter, Andreas, DC Optimal Power Flow with Joint Chance Constraints, IEEE Transactions on Power Systems 36(1):147-158 (2021).
- Jara-Moroni, Francisco; Mitchell, John E.; Pang, Jong Shi; Wächter, Andreas, An enhanced logical benders approach for linear programs with complementarity constraints, Journal of Global Optimization 77(4):687-714 (2020).
- Ben Feng, M.; Maggiar, Alvaro; Staum, Jeremy C; Wächter, Andreas, Uniform convergence of sample average approximation with adaptive multiple importance sampling, Institute of Electrical and Electronics Engineers Inc.:1646-1657 (2019).
- Feng, Mingbin; Mitchell, John J.; Pang, Jong Shi; Shen, Xin; Waechter, Andreas, Complementarity Formulations of ℓ0-norm Optimization, Pacific Journal of Optimization 14(2):273-305 (2018).
- Curtis, Frank E.; Wächter, Andreas; Zavala, Victor M., A sequential algorithm for solving nonlinear optimization problems with chance constraints∗ , SIAM Journal on Optimization 28(1):930-958 (2018).
- Gao, Hanyu; Waechter, Andreas; Konstantinov, Ivan A.; Arturo, Steven G.; Broadbelt, Linda J, 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 108:268-275 (2018).
- Maggiar, Alvaro; Wächter, Andreas; Dolinskaya, Irina; Staum, Jeremy C, A derivative-free trust-region algorithm for the optimization of functions smoothed via Gaussian convolution using adaptive multiple importance sampling∗ , SIAM Journal on Optimization 28(2):1478-1507 (2018).