Accelerated Image Processing and Optimization

Designed and implemented proximal gradient descent, stochastic subgradient methods, ADMM, and coordinate descent for L1/L2 regression, logistic regression, image denoising, deblurring, and matrix completion.

The project emphasized solver performance, accuracy, and convergence behavior. Using Gurobi and careful parameter tuning, experiments scaled to substantially more problem instances while reducing runtime for large-scale problems by up to 60% without sacrificing reconstruction quality.