20602 - COMPUTER SCIENCE (ALGORITHMS)

Prerequisites: To feel comfortable in this course you should have a good knowledge of calculus, statistics, and probability theory.

Scope of the course is to provide the basic conceptual tools at the root of basic classes of optimization and sampling algorithms. These notions and method which are extensively used in subsequent courses of the education program.

• Review of complexity theory and approximation schemes.
• Optimization algorithms: Linear Programming and integer Programming.
• Dynamics programming.
• Randomized algorithms for optimization.
• Monte Carlo Markov Chains for sampling and optimization.
• Stochastic programming, multi stage optimization.
• On-line algorithms over networks.

• Understand the basic notions of approximation algorithms.
• Analyze the structure of advanced algorithmic chemes for optimization and sampling.
• Illustrate the role of randomness in optimization.
• Master the basic notions of on-line algorithms.

• Develop algorithms for large scale difficult optimization problerms.

• Face-to-face lectures

Lectures.

The exam consists of a single  written problem solving  exercise. The students are asked to formulate rigorously an optimization problem and provide a detailed description of the algorithmic solution  of choice.

The results are used to assess both the  "knowledge and understanding" and the "applying knowledge and understanding" learning   objectives.

• T.H. CORMEN, C.E. LEISERSON, R.L. RIVEST, et al., Introduction to Algorithms, MIT, 3rd edition.
• C. MOORE, S. MERTENS, The Nature of Computation, Oxford.
• R. MOTWANI, P. RAGHAVAN, Randomized algorithms, Cambridge University Press New York, NY, USA.
• D. BERTSEKAS, Nonlinear programming, Athena Scientific, 3rd edition.