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This course is designed to address the fundamental mathematical theories for complex engineering system (product) design optimization in multidisciplinary environment. The course starts with the basics of nonlinear programming (continuous optimization), then expands to the area of multidisciplinary design optimization (MDO) in depth. Analytical Target Cascading (ATC) - a well-established hierarchical optimization method - is covered in-depth with assignments in written and programming forms. After a successful completion of the course, the students will be able to model and solve basic MDO problems and apply MDO in a research-based semester project. Prior experience in coding (in Matlab or similar) will be helpful but not required. Course Information: 4 graduate hours. No professional credit. Prerequisite: IE 310.

The course targets fundamental theories of multidisciplinary design optimization (MDO). Design and optimization of complex systems is challenging. One of the core methods is MDO where a large-scale complex systems are decomposed into small-scale subsystems. Two core questions arise in MDO – solution equivalence and convergence to optimum. The course brings core theories of MDO with a basis in nonlinear programming, system decomposition, convergence characteristics study, analytical target cascading, and quasi-separable decomposition. Solution engines will be also studied including Newtonian methods, trust-region methods, and data-driven analytics methods. Students are required to complete a semester project either individual or group. Prerequisite: IE 310.