Advances in nonlinear and surrogate optimization | AIChE

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Advances in nonlinear and surrogate optimization

Co-chair(s)

Allman, A., University of Michigan
Cao, Y., The University of British Columbia

Optimization methods algorithms form the core tools for process operations and process systems engineering in general. The session invites papers in the general area of Process Systems Optimization. Topics on theory, algorithms and software for all classes of optimization problems are considered. Application papers offering insights into the interplay between optimization theory and practice are also encouraged.

Presentations

8:00 AM
(558a) A Novel Constrained Bayesian Optimization Method for Computationally Expensive Grey-Box Models with Composite Objective and Constraint Functions
8:19 AM
(558b) A Unifying Abstraction for Infinite-Dimensional Optimization
8:38 AM
(558c) Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs
8:57 AM
(558d) Learning the Structure of Optimization Problems through Stochastic Blockmodeling
9:16 AM
(558e) A New Reinforcement Learning Based Bayesian Optimization Method for a Sequential Decision Making in an Unknown Environment
9:35 AM
(558f) A Nested Schur Decomposition Approach for Multiperiod Optimization of Chemical Process
9:54 AM
(558g) Model-and-Search: A Derivative-Free Local Optimization Algorithm
10:13 AM
(558h) Safe Real-Time Optimization Using Multi-Fidelity Gaussian Processes

Topics 

Computing and Systems Engineering

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Pricing

Individuals

AIChE Pro Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
AIChE Explorer Members $225.00
Non-Members $225.00

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