STUDY NOTES
Structured notes that consolidate the mathematical tools, control methods, and learning algorithms I use in research. The list is ordered from newest to oldest.
Learning & Decision Making
A from-first-principles guide to reinforcement learning: MDPs, returns, Bellman equations, temporal-difference learning, DQN, policy gradients, actor–critic, PPO, and constrained/safe RL.
Optimization & Control
A complete path from dynamic models and finite-horizon optimization to receding-horizon implementation, recursive feasibility, stability, robust/tube MPC, nonlinear MPC, and distributed MPC.
Systems & Control
A self-contained route through state-space modeling, equilibria and linearization, eigenvalue stability, controllability, observability, state feedback, LQR, observers, Kalman filtering, and reference tracking.
Convex Optimization
A from-first-principles guide to projection-free constrained convex optimization: geometry, linear minimization oracles, step sizes, the Frank–Wolfe gap, convergence, active sets, away/pairwise variants, and practical use cases.
