Page Updated January 14, 2026
Nonlinear Optimization: Techniques for Engineering
Presented by R. Russell Rhinehart, at the 2026 American Control Conference, May 26, 2026.
The American Control Conference (ACC) is the annual conference of the American Automatic Control Council (AACC). It will be held May 26-29, 2026 in New Orleans, Louisiana at the Hilton New Orleans Riverside.
Nine professional societies co-sponsor the ACC: AIAA, AIChE, ASCE, ASME, IEEE-CSS, INFORMS, ISA, SCS, SIAM. The ACC is a 4-day event with about 1,300 attendees presenting and discussing research innovations in control for many disciplines. Part of the conference is a full day of workshops (short courses) intended to bridge the research-practice gap to benefit both practitioner and innovator attendees. These may be the most relevant part of the programming for practitioners. Workshops are scheduled for May 26, from 8:30 AM to 5:00 PM.
To see the workshop topics, visit https://acc2026.a2c2.org/ then use the “Program” link then “Workshops” (the active link is coming soon).
To register visit https://acc2026.a2c2.org/registration (the active link is coming soon). You can register for workshops independent of the conference.
About the Nonlinear Optimization Workshop (Short Course):
Optimization is fundamental for advanced control – in solving for action in constraint-handling model-predictive control, for adjusting model coefficients in generating digital twins or empirical models from data, and for supervisory setpoint optimization. Optimization is also essential in design of processes, equipment, and products. This full-day workshop will be a practical guide for those using multivariable, constraint-handling, nonlinear optimization. Although concepts and mathematics of fundamental methods will be revealed, the takeaway will be participants’ ability to apply optimization within their context.
The workshop will cover common gradient-based optimization techniques (Steepest Descent, Sequential Line Search, Newton, Levenberg-Marquardt, GRG, etc.) and direct-search techniques (Heuristic, Hook-Jeeves, Particle Swarm, Leapfrogging, Genetic Algorithms), and both single and multi-objective applications (Pareto Optimal). These are chosen for their utility and because they represent the fundamentals of most approaches.
The intended audience is engineering employees, graduate students, and faculty who need to use nonlinear optimization.
Course examples will represent diverse applications which should be understood by any within the engineering disciplines. Participants will receive course notes and software to provide exercises and access to code. Exercises and code can be implemented in any environment, but Excel/VBA will be used as in-workshop examples and exercises. Participants are invited to bring a computer with Excel version 2010 or higher for in-class exploration, and they have permission to directly apply the provided software to their specific problems. The material is based on the textbook by Rhinehart, Engineering Optimization: Applications, Methods, and Analysis, 2018, John Wiley & Sons. Visit https://www.r3eda.com/ for a sampling of the material.
Most exercises are for 2-dimensional applications for visual understanding of the surfaces and methods, but the methods and techniques are scalable to high dimension. Several N-D examples will be provided.
Rationale:
Optimization means seeking the best outcome or solution, and is a fundamental tool for modeling, model-based control, forecasting, design, analysis and diagnosis, supervisory economic operation, safety, precision, design, sustainability, etc. We desire an efficient procedure to find the best solution with minimal computational and experimental effort.
Part of this short course is about the search logic, the optimizer algorithm. However, the major challenges in optimization are often not the intellectually stimulating mathematics of the algorithm.
Instead, the major challenges relate to the clear and complete statement of
- the objective function (the outcome you wish to minimize or maximize),
- constraints (what cannot be violated, exceeded, etc.),
- the decision variables (what you are free to change to seek a minimum),
- the model (how DVs relate to OF and constraints),
- the convergence criterion (the indicator of whether the algorithm has found the min or max and can stop, or needs to continue),
and, to choosing
- the DV initialization (what locations, values),
- the number of starts from randomized locations to be confident that the global optimum has been found, and
- the appropriate optimization algorithm (for the function aberrations, for utility, for precision, for efficiency).
This course addresses all those elements. Understanding how the algorithm works is essential to choosing the right optimizer for your application.
Prerequisite skills
Any undergraduate engineering or mathematics program should have provided the participant with an adequate experience in calculus, analytical geometry, linear algebra, vector/matrix notation, statistics, and computer programming. The course will review essential topics that are commonly un-remembered from undergraduate courses. A more important prerequisite is understanding the application – its behavior, attributes, and context.
Workshop Schedule:
AM – Session 1
Introductory Concepts and Definitions
Gradient based concepts – Cauchy, Incremental Steepest Descent, Newton
Problems and Improvements – Levenberg-Marquardt.
Problems and Constraints
Break
AM – Session 2
Penalty Weighting
Multiple Optima
Multiple Starts
Lunch
PM – Session 1
Convergence Criteria
Surface aberrations – discontinuities, stochastic, narrow valleys
Simple Direct Methods – Heuristic Cyclic, Hook-Jeeves
Multi-Player Direct Methods – Leapfrogging, Particle Swarm
Break
PM – Session 2
Comparisons
Objective Function Formulation
End
Presenter Bio:
This workshop will be presented by Russ Rhinehart.
Dr. R. Russell Rhinehart has experience in both industry (13 years) and academe (31 years). He was Head of the School of Chemical Engineering at Oklahoma State University for 13 years and retired in 2016 to shift his career toward professional education. Russ was a president of the American Automatic Control Council and Editor-in-Chief of ISA Transactions. He is a Fellow of both AIChE and ISA, a CONTROL Automation Hall of Fame inductee, and received numerous teaching and innovation recognitions.
His 1968 B.S. in Chemical Engineering and subsequent M.S. in Nuclear Engineering are both from the University of Maryland. His 1985 Ph.D. in Chemical Engineering is from North Carolina State University.
He is coauthor of the textbook Applied Engineering Statistics, and author of three other books: Nonlinear Regression Modelling, Nonlinear Model-Based Control, and Engineering Optimization. The last book is the basis of this short course. He authored seven handbook chapters on modeling, uncertainty, process control, and optimization; and published over 270 articles including 65 in the Develop Your Potential Series in CONTROL magazine.
Russ also developed a web site to support his aim to disseminate best-in-class public-domain techniques for modeling, optimization, and control. You are invited to visit www.r3eda.com.