The aim of this thesis is mapping of current state of teaching of algorithmization in secondary school. Basic information about the subject is given in theoretical part - a relation between teaching of algorithmization and RVP ZV, and basic assupmtions of teaching of algorithmization: a historical aspect, methods, aims and possibilities of teaching of algorithmization (and an approach on teaching). The essential part of teaching of algorithmization is an education support - a textbook about algorithmization and programming, programming support, projects and competitions for students. Practical part is a pedagogical research. This research consists of five themes A. A quantitative research of teaching of algorithmization in secondary school in České Budějovice. B. A review of extra curricural teaching of algorithmization in ČB. C. A statewide mapping of teaching of algorithmization in Czech Republic. D. A statewide research on a success rate of the competition ?Bobřík informatiky? (informatics beaver). E. A teacher's experience with a textbook.

The diploma thesis focuses on decision making in business practice. The thesis deals with design of the tool which should make the solution of decision tasks easier by usage of fuzzy logic, decission trees and linear programming.

Abstract:! The aim of this thesis is to find the optimal control of Markov chain with discounted evaluation of transitions in discrete and also in continuous time. We present Howard's iterative algorithm, the algorithm for finding the optimal control. Then the strategy is applied to the problem of optimal trading, where the goal is to maximize market price of the portfolio in infinite time horizont, given the existence of the proportional transaction costs. Market price is simulated with Brownian motion.

Title: The knapsack and its applications Author: Romana Linkeová Department: Department of Algebra Supervisor: doc. Mgr. Pavel Příhoda, Ph.D., Department of Algebra Abstract: This thesis is focused on various aspects of cryptosystems based on NP (non-deterministic polynomial) complete knapsack problem. From the theory of complexity point of view, the less known parts of the proof of knapsack problem NP completeness are shown in detail. From the cryptographical point of view, a demonstration of breaking of the Merkle-Hellman cryptosystem (the basic de- sign of knapsack-type cryptosystems) is provided, showing that poor parameters choice can lead to easy obtaining of the whole private key. Another contribution of this thesis consists in a presented proposal of a new cryptosystem concept based on the matrix 0-1 knapsack problem. This concept was developed in order to prevent known attacks, however, in the thesis we provide a proof analogous to J. C. Lagarias and A. M. Odlyzko, 1985, which shows that an attack based on the LLL algorithm will be successful on the majority of the matrix 0-1 kna- psack problem cryptosystems. Finally, a list of modern cryptosystems based on the knapsack problem is provided and a cryptanalysis thereof is given. Keywords: knapsack problem, NP complete problems, LLL algorithm 1

The thesis first addresses the typology of cutting problems and their relationship to the packing problems. These are categorized (Wascher et al (2005)) according to 5 basic kriteria into the so-called "refined problem types", which is the sufficiently detailed and practical segmentation of cutting problems. The thesis deals with a selected sample of some of the most interesting algorithms from the wide range of those used to solve the cutting problems. The Viswanathan-Bagchi algorithm for the exact solution of constrainted two-dimensional orthogonal Cutting stock probléme with gillotine cuts is briefly described. It enables to process a wide range of additional problem constraints. The body of the thesis concentrates on heuristic algorithms used to solve orthogonal Open dimension problems. The Best-fit algorithm according to Burke et al. (2004) is described in detail. The work introduces two modifications of this algorithm that helped improve the solution in 42 out of the 89 benchmark problems, while a worse solution was achieved only in 10 of them. Moreover, new and more effective data structures and procedures that enable to solve the testing exercise with approx 50 000 rectangles in just about 2,5 seconds have been introduced.

The objective of this thesis is to apply alternative multi-objective optimization techniques to the portfolio selection problem. Theoretical part starts with detailed analysis of the classical Markowitz model and its assumptions. Following that, introduction of multi-criterion optimization techniques available for finding non-dominated portfolios is given. One of these techniques, the genetic algorithm, is presented in great detail. Some of the basic methods useful for predicting stock prices and its risks are presented at the end of the theoretical part. Practical part presents an application of the theory to the problem of constructing efficient portfolios of 11 selected stocks traded on Prague Stock Exchange. Results achieved by different approaches are compared and interpreted.

This thesis is intent on using mixtures of probability distributions in generalized linear model. The theoretical part is divided into two parts. In the first chapter a generalized linear model (GLM) is defined as an alternative to the classical linear regression model. The second chapter describes the mixture of probability distributions and estimate of their parameters. At the end of the second chapter, the previous theories are connected into the finite mixture generalized linear model. The last third part is practical and shows concrete examples of these models.

This thesis describes the design and implementation of genetic algorithm for approximation of non-linear mathematical functions using the least squares method. One objective of this work is to theoretically describe the basics of genetic algorithms. The second objective is to create a program that would be potentially used to approximate empirically measured data by the scientific institutions. Besides the theoretical description of the given subject, the text part of the work mainly deals with the design of the genetic algorithm and the whole application solving the given problem. Specific part of the assignment is that the developed application has to support approximation of points by various mathematical non-linear functions in several different intervals, and then it has to insure, that resulting functions are continuous throughout all the intervals. Described functionality is not offered by any available software.

Object of this thesis is to construct a mixture model of earnings of the Czech households. In first part are described characteristics of mixtures of statistical distributions with the focus on the mixtures of normal distibutions. In practical part of this thesis are constructed models with parameters extimations based on the data from EU-SILC. Models made by graphical method, EM algorithm and method of maximum likelihood. The quality of models is measured by Akaike information criterion.

Using Gaussian mixtures is a popular and very flexible approach to statistical modelling. The standard approach of maximum likelihood estimation cannot be used for some of these models. The estimates are, however, obtainable by iterative solutions, such as the EM (Expectation-Maximization) algorithm. The aim of this thesis is to present Gaussian mixture models and their implementation in R. The non-trivial case of having to use the EM algorithm is assumed. Existing methods and packages are presented, investigated and compared. Some of them are extended by custom R code. Several exhaustive simulations are run and some of the interesting results are presented. For these simulations, a notion of usual fit is presented.