Publications

Results 26–50 of 76

Search results

Jump to search filters

Benchmarking ADMM in nonconvex NLPs

Computers and Chemical Engineering

Laird, Carl D.; Rodriguez, Jose S.; Nicholson, Bethany L.; Zavala, Victor M.

We study connections between the alternating direction method of multipliers (ADMM), the classical method of multipliers (MM), and progressive hedging (PH). The connections are used to derive benchmark metrics and strategies to monitor and accelerate convergence and to help explain why ADMM and PH are capable of solving complex nonconvex NLPs. Specifically, we observe that ADMM is an inexact version of MM and approaches its performance when multiple coordination steps are performed. In addition, we use the observation that PH is a specialization of ADMM and borrow Lyapunov function and primal-dual feasibility metrics used in ADMM to explain why PH is capable of solving nonconvex NLPs. This analysis also highlights that specialized PH schemes can be derived to tackle a wider range of stochastic programs and even other problem classes. Our exposition is tutorial in nature and seeks to to motivate algorithmic improvements and new decomposition strategies

More Details
TYPE Journal Article YEAR 2018
DOIOSTIScopus

Developing Fugitive Emissions Sensor Networks

Klise, Katherine A.; Nicholson, Bethany L.; Laird, Carl D.; Flanagan, Tatiana P.; Ravikumar, Arvind; Sreedhara, Sindhu; Brandt, Adam

This document summarizes research performed under the Laboratory Directed Research and Development (LDRD) project titled Developing Fugitive Emissions Sensor Networks: New Optimization Algorithms for Monitoring, Measurement and Verification. The purpose of this project is to develop methods and software to enhance detection programs through optimal design of the sensor network. This project includes both software development and field work. While this project is focused on methane emissions, the sensor placement optimization framework can be applied to a wide range of applications, including the placement of water quality sensors, surveillance cameras, fire and chemical detectors. This research has the potential to improve national security by improving the way sensors are deployed in the field.

More Details
TYPE SAND Report YEAR 2018
DOIOSTI

A framework for modeling and optimizing dynamic systems under uncertainty

Computers and Chemical Engineering

Nicholson, Bethany L.; Siirola, John D.

Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.

More Details
TYPE Journal Article YEAR 2018
DOIOSTIScopus
Results 26–50 of 76
Results 26–50 of 76