This full-day workshop will be a practical guide for those using multivariable, constraint handling, nonlinear optimization. Although theoretical analysis behind techniques will be revealed, the takeaway will be participants’ ability to:

Define the objective function (cost function)
Incorporate hard constraints in the search
Convert constraints to appropriately weighted penalties
Choose an appropriate optimizer
Choose appropriate convergence criteria and thresholds
Choose initialization and number of trials to be confident in finding the global optimum
The short-course will cover common gradient-based optimization techniques (Incremental Steepest Descent, Cauchy, Newton, Levenberg-Marquardt) and direct-search techniques (Heuristic, Hook-Jeeves, Particle Swarm, and Leapfrogging) that represent the fundamentals of most approaches. This workshop is based on a popular interdisciplinary graduate engineering course. Participants will receive course notes of approximately 200-pages, and software to provide exercises and access to code. Exercises and code can be implemented in any environment, but Excel/VBA will used as in-workshop examples and exercises. Participants are invited to bring a computer with Excel version 2010 or higher for in-class exploration. There are currently more than 80 functions and over 20 optimization algorithms in the simulator.

The major challenges in optimization are often not 1) the mathematics of the algorithm, but the clear and complete statement of the 2) objective function, 3) constraints, 4) decision variables, 5) models, 6) convergence criterion, 7) initialization, 8) starting points, and 9) optimization algorithm. This short-course addresses all nine of the elements.