Convex Optimization

#Learning Objectives

• Test whether a function is convex using the definition, second derivative, and Hessian criteria
• Implement Newton's method and compare its quadratic convergence against gradient descent
• Solve constrained optimization problems using Lagrange multipliers and interpret KKT conditions
• Explain why neural network loss landscapes are non-convex yet SGD still finds good solutions