2021 Annual Meeting

Session: Advances in nonlinear and surrogate optimization

Co-Chairs

Andrew Allman, University of Michigan
Yankai Cao, The University of British Columbia

Presentations

08:00 AM

(558a) A Novel Constrained Bayesian Optimization Method for Computationally Expensive Grey-Box Models with Composite Objective and Constraint Functions
Joel Paulson, Congwen Lu

08:19 AM

(558b) A Unifying Abstraction for Infinite-Dimensional Optimization
Joshua Pulsipher, Weiqi Zhang, Victor M. Zavala

08:38 AM

(558c) Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs
Sungho Shin, Mihai Anitescu, Victor M. Zavala

08:57 AM

(558d) Learning the Structure of Optimization Problems through Stochastic Blockmodeling
Ilias Mitrai, Prodromos Daoutidis

09:16 AM

(558e) A New Reinforcement Learning Based Bayesian Optimization Method for a Sequential Decision Making in an Unknown Environment
Mujin Cheon, Jay H. Lee, Ha-Eun Byun

09:35 AM

(558f) A Nested Schur Decomposition Approach for Multiperiod Optimization of Chemical Process
Noriyuki Yoshio, Lorenz Biegler

09:54 AM

(558g) Model-and-Search: A Derivative-Free Local Optimization Algorithm
Luis Miguel Rios, Kaiwen Ma, Nikolaos Sahinidis

10:13 AM

(558h) Safe Real-Time Optimization Using Multi-Fidelity Gaussian Processes
Panagiotis Petsagkourakis, Benoit Chachuat, Antonio del Rio Chanona