Info
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Course 2019-2020 a.y.

20602 - COMPUTER SCIENCE (ALGORITHMS)

DSBA
Department of Decision Sciences

Course taught in English

Go to class group/s: 31

DSBA (6 credits - II sem. - OBCUR  |  FIS/02)
Course Director:
RICCARDO ZECCHINA

Classes: 31 (II sem.)
Instructors:
Class 31: RICCARDO ZECCHINA


Suggested background knowledge

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


Mission & Content Summary
MISSION

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

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

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Understand the basic notions of approximation algorithms.
  • Analyze the structure of advanced algorithmic schemes for optimization and sampling.
  • Illustrate the role of randomness in optimization.
  • Master the basic notions of on-line algorithms.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Develop algorithms for large scale difficult optimization problerms.

Teaching methods
  • Face-to-face lectures
DETAILS

Lectures.


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
    ATTENDING AND NOT ATTENDING STUDENTS

    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.


    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS
    • 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.
    • Lecture notes by the instructors.
    Last change 05/06/2019 22:27