Optimization is a fundamental tool for modeling, control, forecasting, design, safety, sustainability, etc. We desire an efficient procedure to find the best solution with minimal computational or experimental effort. This workshop is intended to be a practical guide of best practices from conventional methods. Examples will illustrate the choices and techniques. Supporting theory will be addressed, but the take-away will be the ability to implement optimization – to specify objective functions, include constraints, select an appropriate optimizer, and specify initialization and convergence criteria. The course will cover common gradient-based optimization techniques (Newton, Levenberg-Marquardt), surrogate model (successive quadratic), and direct-search techniques (Heuristic, Particle Swarm, and Leapfrogging), representing the fundamentals of most approaches. Illustrative examples and exercises will include dynamic modeling and constrained control. Mostly, examples represent mechanical situations, so that people from all engineering and computer science disciplines can understand. Participants will receive a draft textbook (Wiley, anticipated late 2017) and software in Excel VBA, which will provide exercises and access to code. Some course material can be previewed on www.r3eda.com. Participants are invited to bring a computer with Excel version 2010 or higher for in-class exploration. The programs are written by the workshop presenter, and can accommodate up to 20 decision variables. Participants are free to use the software subsequently, or to migrate it to their preferred language.