We present a machine learning (ML) framework to optimize the specificity and speed of
liquid crystal (LC)-based chemical sensors. Specifically, we demonstrate that ML techniques …

We present graph-based modeling abstractions to represent cyber-physical dependencies
arising in complex systems. Specifically, we propose an algebraic graph abstraction to …

First, we point out that available decomposition-based control and optimization algorithms
are essentially based on some I block structure i in the underlying I network i topology of the …

We present a general graph-based modeling abstraction for optimization that we call an
OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical …

This work develops an algorithm for solving nonlinear multiperiod optimization (MPO)
problems using a nested Schur decomposition (NSD) approach. The NSD approach …

The optimal design of experiments is crucial for maximizing the information content of data
across a wide-range of experimental goals. This paper presents a Bayesian approach to …

We study connections between the alternating direction method of multipliers (ADMM), the
classical method of multipliers (MM), and progressive hedging (PH). The connections are …

We present a nonlinear programming (NLP) framework for the scalable solution of
parameter estimation problems that arise in dynamic modeling of biological systems. Such …

We study the convergence properties of an overlapping Schwarz decomposition algorithm
for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time …

Large-scale numerical optimization problems arise from many fields and have applications
in both industrial and academic contexts. Finding solutions to such optimization problems …