1 Optimization.- 1.1 Stochastic optimization problems.- 1.2 Approximations.- 1.3 Bounds.- 1.4 Deterministic optimization procedures and their stochastic counterparts.- 1.5 Discrete Optimization.- 2 Discrete-Event processes.- 2.1 Markov chains with discrete time.- 2.2 Markov chains with continuous time.- 2.3 MARKOV PROCESSES WITH ARBITRARY STATE SPACE.- 2.4 Semi-Markov processes.- 2.5 Generalized Semi-Markov processes.- 2.6 Queueing processes.- 3 Derivatives.- 3.1 Derivatives of random processes.- 3.2 Derivatives of probability measures.- 3.3 Derivative concepts for Markov processes.- 4 Simulation and sensitivity estimation.- 4.1 Simulation techniques.- 4.2 Simulation of derivatives for random variables.- 4.3 Simulation of sensitivities for Markov Processes.- 4.4 Estimation of derivatives of Discrete-Event processes.- 5 Stochastic Approximation.- 5.1 Convergence and asymptotic distributions.- 5.2 Stopping times and confidence regions.- 5.3 DISCRETE STOCHASTIC APPROXIMATION.- 5.4 Stochastic Approximation versus the Response Surface Method.- 5.5 The convergence of the combined procedure.- A Metric spaces.- B Sequences and Series.- C Matrix Algebra.- D Derivatives.- E Convexity and convex projections.- F Set-wise Convergence.- G Duality and Lagrangians.- H Probability spaces and random variables.- I Convergence of random variables.- J The Wasserstein distance.- K Conditional expectations.- L Martingales.- M Choquet Theory.- N Coupling.