Contents. 1. Introduction: applications and issues. 2. Applications to learning, state dependent noise, and queueing. 3. Applications in signal processing in adaptive control 4. Mathematical background. 5. Convergence with probability one: martingale difference noise. 6. Convergence with probability one: correlated noise. 7. Weak convergence: introduction. 8. Weak convergence methods for general algorithms. 9. Applications: proofs of convergence. 10. rate of convergence. 11. Averaging of the iterates. 12. Distributed/decentralized and asynchronous algorithms
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