Computational Optimization. 3 credits, 3 contact hours

Catalog description

Prerequisites: The course assumes knowledge of algorithms and probability, or equivalent coursework. This course covers the theoretical foundations and practical methods of continuous optimization. Students will explore both deterministic and stochastic approaches, with analysis of average-case and asymptotic error bounds, and applications spanning machine learning and data science.