Lectures related to Optimal stopping

Explores stopping times, the optional stopping theorem, F-measurable random variables, and martingales.

Covers the optional stopping theorem for martingales, providing a detailed proof and discussing its implications.

Explores American derivatives pricing, hedging, and replication strategies in financial markets.

Explores sub- and supermartingales, stopping times, and their applications in stochastic processes.

Explores martingale transforms, their adaptability to filtrations, interpretation, and application in predicting future outcomes.

Covers the reflection principle and martingale writing in simple symmetric random walks.

Explores three stopping theorems in martingales and Brownian motion.

Explores the optional stopping theorem for martingales and stepping times, emphasizing its applications and implications.

Covers optimization methods, central path algorithms, optimality conditions, and Newton's method.

Covers Newton's method on Riemannian manifolds, focusing on second-order optimality conditions and quadratic convergence.

Explores the proof of the martingale convergence theorem and the concept of stopping time in square-integrable martingales.

Explores extreme points in linear programming and the role of constraints in finding optimal solutions.

Covers the Bellman equation, Q-values, discount factor, and optimal actions.

Explores self-financing strategies, asset pricing theorems, and arbitrage opportunities in financial markets.

Discusses policy iteration and linear programming methods for solving Markov Decision Processes.

Explores entropy's role in coding strategies and search algorithms, showcasing its impact on information compression and data efficiency.

Covers the Euler equation, dynamic programming, and optimal consumption in portfolio allocation.

Covers the theory and applications of optimal transport, focusing on infimal convolution and Kantorovich potentials.