Optimization Techniques for Electrical and Computer Engineering

Catalog description

Explores optimization techniques and applications in computing systems and machine learning. Topics include integer/mixed-integer programming, simplex method, gradient descent, quasi-Newton methods (BFGS, L-BFGS), and chance-constrained optimization. Students apply these in real-world cases like GPU resource management, FPGA design, embedded systems, network optimization, energy-efficient scheduling, and AI model training. Recommended: coursework in data structures; algorithms; pro algorithms; probability and statistics; and Python programming.