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Lecturer(s)
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Machalová Jitka, doc. RNDr. Ph.D., MBA
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Burkotová Jana, Mgr. Ph.D.
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Course content
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1. Polynomial splines 2. B-splines and their basic properties 3. Interpolation with splines 4. Splines in leas square problem 5. Smoothing splines 6. Tensor product splines 7. Splines on triangulations 8. Interpolation of data in v R^2 9. Data smoothing in R^2 10. Spline curve 11. Splines in Hilbert space
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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Gain knowledge about approximation of data by using splines.
Knowledge Gain useful knowledge about approximation of data by using splines.
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Prerequisites
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Standard knowledge from mathematical analysis, linear algebra and numerical methods.
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Assessment methods and criteria
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Oral exam, Seminar Work
Credit: the student has to compute assigned examples. Exam: the student has to understand the subject and be acquainted with theory and computational methods.
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Recommended literature
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C. de Boor. (1978). A Practical Guide to Splines. Springer, New York.
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Ch. Gu. (2013). Smoothing spline ANOVA Models. Springer.
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J. Kobza. (1993). Splajny. skriptum UP, Olomouc.
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K. Najzar. (2006). Základní teorie splinů. skriptum UK, Praha.
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P. Dierckx. (1995). Curve and Surface Fitting with Splines. Oxford University Press, New York.
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