I079 NP-hard num. problems

Faculty of Informatics
Autumn 1999
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
RNDr. Petr Mejzlík, Dr. (lecturer)
Guaranteed by
prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: RNDr. Petr Mejzlík, Dr.
Prerequisites
M001 Calculus II && M004 Linear Algebra II && I012 Complexity
Prerequisites: M001 Calculus II, M004 Linear Algebra and Geometry II and I012 Complexity.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Syllabus
  • This course introduces NP-hard problems encountered in numerical computations and presents aproximate methods for finding their solutions.
  • Global optimization and its aplication in Natural Sciences, engineering design and Computer Graphics. Numerical integration. Aproximation of real-valued functions. Solution of systems of nonlinear equations.
  • Monte Carlo methods. Smoothing transforms. Machine Learning methods. Interval methods.
Literature
  • Global minimization of nonconvex energy functions :molecular conformation and protein folding : DIMACS workshop, March 20-21, 1995. Edited by P. M. Pardalos. Providence, R.I.: American Mathematical Society, 1996, xiii, 271. ISBN 0-8218-0471-5. info
  • Global optimization in engineering design. Edited by Ignacio E. Grossmann. Dordrecht: Kluwer Academic Publishers, 1996, ix, 386 s. ISBN 0-7923-3881-2. info
  • SUTTON, Richard S. and Andrew G. BARTO. Reinforcement learning : an introduction. Cambridge: Bradford Book, 1998, xviii, 322. ISBN 0262193981. info
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.

  • Enrolment Statistics (recent)
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