BSC  IN  MATHEMATICS
During the six-semester Mathematics BSc program students acquire skills in pure and applied mathematics which enable them to pursue successful Master’s studies at inland or foreign leading universities or get employed in different areas of technology, economics, statistics and informatics. Profiting of the environment given by the University of Technology and Economics we train experts who are interested in practical problems and are able to use their knowledge creatively. In addition to being familiar with abstract fields of mathematics, they are able to communicate and collaborate with representatives of other professions. Through extensive relationships of our Institute our students can gain an insight into various fields of applications of mathematics and mathematical modelling of real life problems.
    Students getting a BSc degree in Mathematics at our university can quickly and easily find a decent high-paying job either in Hungary or abroad. Several banks, investment funds, insurance, business consulting companies as well as those engaged in data mining or optimization employ mathematicians in senior positions. After receiving the BSc degree students can be admitted to the Applied Mathematics or Mathematics master program or other MSc programs subject to special conditions.
    Special characteristics of our BSc program are the tutorial system, a large number of homework problems, opportunities to take part in project works and to get involved in high-level scientific research and a significantly higher than average personal attention paid to students thanks to their relatively small number.
CURRICULUM
Code Title Parameters* ECTS credits
per semester
Lc Pr Lb Rq Cr I II III IV V VI
  Obligatory courses (161 ECTS credits) 28 32 32 28 18 21
BMETE91AM35 Basics of Mathematics 2 0 0 v 3 3          
BMETE92AM36 Calculus 1 6 2 0 v 9 9          
BMETE91AM36 Introduction to Algebra 1 6 2 0 v 9 9          
BMETE94AM17 Introduction to Geometry 2 0 0 v 3 3          
BMETE91AM42 Informatics 1 1 0 2 f 4 4          
BMETE92AM37 Calculus 2 6 2 0 v 8   8        
BMETE91AM37 Introduction to Algebra 2 6 2 0 v 8   8        
BMEVISZA025 Combinatorics and Graph Theory 1 2 2 0 v 6   6        
BMETE94AM18 Geometry 4 0 0 v 6   6        
BMETE91AM43 Informatics 2 1 0 2 f 4   4        
BMETE13AM16 Physics 1 for Mathematicians 2 0 0 f 2     2      
BMEGT35A410 Accounting 2 0 0 f 3     3      
BMETE92AM38 Analysis 1 3 2 0 v 7     7      
BMETE91AM38 Algebra 1 3 2 0 v 7     7      
BMETE95AM29 Probability Theory 1 2 2 0 v 6     6      
BMETE91AM46 Programming Exercises for Probability Theory 0 0 0 f 1     1      
BMETE93AM15 Differential Equations 1 2 2 0 v 6     6      
BMETE91AM44 Informaitcs 3 2 0 2 f 4       4    
BMETE95AM31 Mathematical Statistics 1 2 0 2 v 5       5    
BMETE92AM39 Analysis 2 2 2 0 v 5       5    
BMETE94AM19 Differential Geometry 1 2 1 0 f 4       4    
BMETE93AM19 Operations Research 2 2 0 v 5       5    
BMEVISZAB01 Theory of Algorithms 2 2 0 v 4       4    
BMETE91AM47 Programming Exercises forTheory of Algorithms 0 0 0 f 1       1    
BMETE91AM39 Algebra 2 4 0 0 v 4         4  
BMETE93AM16 Optimization Models 2 0 2 f 4         4  
BMETE95AM34 Stochastic Processes 5 0 0 v 6         6  
BMETE95AM12 Creating Mathematical Models 0 2 0 f 2         2  
BMEGT30A410 Micro- and Macroeconomics 3 0 0 f 4         4  
BMETE92AMxx Applied Numerical Methods with Matlab 2 0 2 f 4           4
BMETE94AM20 Differential Geometry 2 3 1 0 v 4           4
BMEGT35A411 Finance 2 0 0 f 3           3
BMETE90AM47 BSc Thesis Project 0 0 10 f 10           10
  Specialization courses (10 ECTS credits must be completed)                   8 4
BMETE95AM33 Tools of Modern Probability Theory 4 0 0 v 4         4  
BMETE92AM42 Measure Theory 4 0 0 v 4         4  
BMETE90AM48 Individual Research Project 1 0 0 0 f 2         2  
BMETE92AM45 Partial Differential Equations 2 2 0 v 4           4
BMETE94AM22 Convex Geometry 2 2 0 v 4           4
BMEVISZA026 Combinatorics and Graph Theory 2 2 2 0 v 4           4
BMETE90AM49 Individual Research Project 2 0 0 0 f 2           2
  Elective courses (9 ECTS credits must be completed)         5 4
*Parameters:
 
Lc = lecture, Pr = practice, Lb = laboratory (hours per week);
 
Rq = requirement or exam type (v = examination, f = midterm exam);
 
Cr = ECTS credits.
Preliminary requirements can be seen at the subjects below.
DESCRIPTION OF SUBJECTS
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM35 Basics of Mathematics 2 0 0 v 3 3          
Course coordinator: Dr. Miklós Ferenczi
Descripton: Notations, formal languages, formalism in mathematics. Mathematics and the deductive systems. Propositional logic. The language of propositional logic. Logical operations, tautologies, logical equivalences. A calculus in propositional logic. Completeness and its importance. First order logic. Language of first order logic: terms, formulas, quantifiers, equality. Structure, model, algebra. Valuation in a model. The concept of logical consequence. Axioms and theorems. Standard and non-standard models. Calculus, deductive and refutation systems. Completeness.  Direct and indirect proofs. On the concepts induction and recursion. The real numbers as ordered field with suprema. The construction of the real numbers. Non-standard real numbers, infinitesimals. Set theory. Ordered pairs, relations, functions. Equivalence- and ordering relations. Equivalence of sets. Countable and non-countable cardinalities. Cantor’s diagonalization procedure. Continuum hypothesis. Classes, Russel paradoxon. Well-ordering. The axiom of choice and its importance.
Literature:
– R.G. Exner: An Accompaniment to Higher Mathematics, Springer, 1996.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE92AM36 Calculus 1 6 2 0 v 9 9          
Course coordinator: Dr. Miklós Horváth
Descripton: Real numbers, sets and mappings. Important inequalities. Real sequences and limits. Convergent and divergent sequences. Monotone and bounded sequences. Subsequences, accumulation points. Theorems of Bolzano and Weierstrass, limsup, liminf. Cauchy theorem. Important limits. Numerical series; convergence and properties. Series of positive numbers. Comparison test, ratio test, nth root test. Absolute and non-absolut convergent series. Alternating series, Leibniz series. Estimations for series. Product of series. Theorem of Mertens and Abel. Real functions. Limits and continuity. Continous functions on bounded closed intervals. Theorems of Bolzano and Weiersrass. Uniformly continous functions, Heine's theorem. Differentiation. Properties of derivatives. Inverse functions. Higher derivatives. Mean value theorems. Elementary functions. Polinomials, exponential, logarithm, trigonometric functions. Function tests, sketching the graphs of functions. Taylor polinomial. Indefinite integral (antiderivatives). Techniqus of integraton. Integration by parts, substitutions, trigonometric integrals, partial fractions. Riemann integral. Propertiesof the integral, upper, lower sums and oscillation sums. Connection with the derivative, Newton-Leibniz rule. Applications of the integral. Mean value theorem. Improper integral.
Literature:
– P.D. Lax, M.S. Terrell: Calculus with applications.
– S. Lang: A first course in calculus.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM36 Introduction to Algebra 1 6 2 0 v 9 9          
Course coordinator: Dr. Erzsébet Horváth
Descripton: Elementary number theory: integers, divisibility, division with remainders, greatest comon divisor, Euclidean algorithm, irreducible numbers and prime numbers. Fundamental Theorem of Arithmetic. Linear Diophantine equations, modular arithmetic, complete and reduced remainder systems, solution of linear congruences. Complex numbers, algebraic and trigonometric forms, Binomial Theorem. Relation between the  complex numbers and the geometry of the plane. Roots of unity, primitive roots of unity. Polynomials with one variable, operations, Horner-scheme, rational root test, Fundamental Theorem of Algebra. Irreducibility of polinomials, Schönemann-Eisenstein criterion. Multivariate polynomials, complete and elementary symmetric polynomials, Viete formulas, roots of cubic polynomials. Systems of linear equations in two and three variables, Gaussian and Gauss-Jordan elimination. R^n and its subspaces. Linear combinations, linear independence, spanned subspace, basis, dimension. Coordinate systems, row space, column space, nullspace of a matrix. Subspace of solutions, solutions in the row space. Matrix operations, inverse matrix, base change matrix. Operations with special matrices, PLU decomposition. Solution of systems of equations with the help of PLU decomposition. Determinant as the volume of the parallelepiped. Basic properties, determinant of a matrix. The notion of  permutations, transpositions, cycles, expansion of the determinant. Laplace Expansion Theorem, Mutiplication Theorem of Matrices, formula for the inverse of a matrix, Cramer's Rule. Basic properties of matrix rank. Linear maps and their matrices: the matirx of a projection to a subspace. Similar matrices. Optimal solution of inconsistent systems of linear equations, normal equation, solution in the row space and its minimality. Moore-Penrose generalized inverse.
Literature:
– W. Sierpinski: Elementary theory of numbers, North Holland, 1987.
– P. Halmos: Finite dimensional vector spaces, Springer, 1967.
– V.V. Prasolov: Problems and Theorems in Linear Algebra, AMS, 1994.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE94AM17 Introduction to Geometry 2 0 0 v 3 3          
Course coordinator: Dr. Jenő Szirmai
Descripton: Euclid's Axioms and Postulates, Hilbert's axioms, points, straight lines, planes, distances, angles etc. Euclidean plane: Geometric transformations, synthetically. Vector geometry, linearly dependent, linearly independent vectors,  scalar and cross product, Cartesian coordinate system, Lagrange-Jacobi identities. Coordinate geometry,  analytic  description of  planes and straight lines, distances, angles, etc. Euclidean space: Geometric transformations (congruences), analytically. Homogeneous coordinates, uniform treatment of geometric transformations. Affinities, similarities. Spherical geometry: geodesic curves, angles,  angle-sum formula for spherical triangles, spherical trigonometry. Definition of polyhedra, Euler theorem. Special polyhedra: convex, regular polyhedra,  Archimedean solids, Catalan solids  etc. Cauchy's rigidity theorem, and other interesting polyhedra.
Literature:
– G.A. Jennings: Modern geometry with applications, Springer-Verlag.
  H.S.M. Coxeter: Introduction to Geometry, New York, Wiley.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM42 Informatics 1 1 0 2 f 4 4          
Course coordinator: Dr. Ferenc Wettl
Descripton: The aim of the course is to study the basic notions of information technology. Basics of hardware (CPU, memory, mass storage,...), the hardware environment of the Institute. Basics of operating systems: program, process, file, folder, file system of Linux and Windows (bash, mc, Windows Total Commander). Graphic user interface, terminal user interface, bash language. Internet, network, IP address, wifi, Internet security. Data on machine: number representation, character encodings. Computer algebra, symbolic calculation (Sage, Mathematica,...), variable, recursion instead of iterative programming, deepening the secondary school function concept (factorial, Fibonacci sequence, Euclidean algorithm, exponentiation, quick exponentiation...). Programming paradigms in computer algebra languages. HTML, the markup language concept, homepage. CSS, separation of the content and presentation. Editing mathematical text: TeX, LaTeX, mathematics on the web. Presentation of math (beamer). Basic concepts of graphic file formats, graphics in mathematical text (TikZ).
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE92AM37 Calculus 2 6 2 0 v 8   8        
Course coordinator: Dr. József Pitrik
Preliminary requirement: Calculus 1
Descripton: Finite dimensional normed vector spaces. Sequences in normed vector spaces, convergence. Theorems of Bolzano and Weierstrass. Multivariable calculus. Continuity. Partial derivatives, directional derivatives. Differentiability and the chain rule.  The differential of a function and its geometrical meaning, linear approximation.Tangent plane and the gradient.  Higher derivatives. Schwarz's theorem. Extremas of multivariable functions. Absolute minima and maxima. Maxima and minima with subsidiary conditions, Lagrange's method of undetermined multipliers. Inverse and imlicit functions. Multiple integrals, fundamental rules. Jordan-measurable sets and their measure. Double integrals, polar transform. Integrals over regions in three and more dimensions. Transformations of multiple integrals. Vector fields and their analysis. Differential calculus of vector fields. Curves and surfaces in three dimension. Line integrals of vector fields. The fundamental theorem of line integrals, independence of path. Potential function. Green's theorem. The Curl and Divergence of a vector field. Parametric surfaces and their areas. Oriented surfaces. Surface integrals of vector fields. Stokes' theorem. The divergence theorem. Sequences and series of functions. Pointwise and uniform convergence. Weierstrass M-test. Consequences of uniform convergence. Power series. Taylor series, binomial series. Fourier series. Inner products on periodic functions. The Fourier and Plancherel theorem. Periodic convolution. Applications.
Literature:
– S. Lang: Undergraduate Analysis.
– E.M. Stein, R. Shakarchi: Fourier Analysis, An Introduction.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM37 Introduction to Algebra 2 6 2 0 v 8   8        
Course coordinator: Dr. Alex Küronya
Preliminary requirement: Introduction to Algebra 1
Descripton: Scalar product and its properties in R^n. Orthogonal and orthonormal bases, Gram-Schmidt ortogonatization process, orthogonal matrices, orthogonal transformations. Householder reflections, Givens rotations. The existence of QR decomposition and its calculation. Optimal solution of systems of linear equations with the help of QR decomposition. Scalar product in C^n. Unitary, normal and selfadjoint matrices and transformations. Eigenvalues, eigenvectors and eigenspaces of matrices and linear transformations. Characteristic equation, solution of the eigenvalue problem. Applications. Algebraic and geometric multiplicity, eigenvalues of special matrices, eigenvalues of similar matrices. Cayley-Hamilton Theorem. Diagonilizability of matrices and its equivalent formulations, (real and complex cases), diagonalizibility of special matrices, relation to the eigenvalues. Unitary and orthogonal diagonalizibility. Schur decomposition, spectral decomposition. Bilinear functions, standard form, signature, Main Axis Theorem. Quandratic forms, definity. Classification of local extrema of a function, geometric applications, graphical presentation. Multilinear functions and maps, total derivative as multilinear map, multivariate Taylor formula, determinant as multilinear function. Singular Value Decomposition of matrices, polar decomposition, applications of SVD, generalized inverse from the SVD. Normal forms of matrices, existence, unicity, determination of the normal form. Generalized eigenvectors, Jordan chain, Jordan basis. Norms of real and complex vectors, matrix norms, basic properties, calculation of norms. Matrix functions (convergence just mentioned, and illustrated), matrix exponential functions. Vector spaces over arbitrary fields. Existence of basis, dimension, infinite dimensional vector spaces (e.g. function spaces), isomorphic vector spaces. Notion of Euclidean space, properties, isomorphism between Euclidean spaces. Dual space. Application of vector spaces over finite fields in coding theory, cryptography and combinatorics.
Literature:
– C.D. Meyer: Matrix Analysis and Applied Linear Algebra, online textbook.
– P. Halmos: Finite dimensional vector spaces, Springer, 1967.
– V.V. Prasolov: Problems and Theorems in Linear Algebra, AMS, 1994.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMEVISZA025 Combinatorics and Graph Theory 1 2 2 0 v 6   6        
Course coordinator: Dr. Tamás Fleiner
Descripton: Enumerative combinatorics (permutations and combinations, binomial theorem, theorems on the binomial coefficients). Significant methods for enumeration, pigeonhole principle and the sieve. Basic Graph Theoretical notions (vertex, edge, degree, isomorphism, path, cycle, connectivity). Trees, Cayley's formula, Prüfer-sequences. Kruskal's greedy algorithm. Characterization of bipartite graphs. Matchings, theorems of Kőnig, Hall and Frobenius, Tutte theorem, Gallai's theorems. Network flows, the Ford-Fulkerson algorithm, Edmonds-Karp algorithm. Menger's theorems, higher vertex and edge connectivity of graphs, Dirac's theorem. Euler's result on Eulerian tours and trails. Hamiltonian cycles and paths, necessary condition for the existence. Sufficient conditions (theorems of Dirac, Ore, Pósa and Chvátal).  Planarity, relation to embeddability on the sphere and the torus, stereographic projection, Euler polyhedron theorem, Kuratowski's theorem, Fáry theorem. BFS and DFS algorithms for shortest paths (Dijkstra, Ford, Floyd), PERT.
Literature:
– R. Diestel: Graph Theory, online available.
– J.A. Bondy,U.S.R. Murty: Graph Theory with Applications.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE94AM18 Geometry 4 0 0 v 6   6        
Course coordinator: Dr. Ákos G. Horváth
Preliminary requirement: Introduction to Geometry
Descripton: Axiomatic methods, introduction to the absolute geometry, hyperbolic, spherical and projective planes. n-dimensional Euclidean geometry, convex polytopes, regular polytopes. n-dimensional classification of surfaces of second-order.
Literature:
– M. Berger: Geometry I-II, Springer 1994.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM43 Informatics 2 1 0 2 f 4   4        
Course coordinator: Dr. Ferenc Wettl
Preliminary requirement: Informatics 1
Descripton: The course aims to learn the programming through understanding the Python language. Introduction to programming and Python language, data types, expressions, input, output. Control structures: if, while. Flowchart, structogram, Jackson figures. Complex control structures. Fundamental algorithms (sum, selection, search extrema, decision..., many practical examples). Lists. For cycle. Newer algorithms (sorting, splitting into two lists...). Exception handling. Abstraction of a part of the program, name it,  using as a building block = function. Function call process, parameters, local variables, passing by value. Abstraction: complex data types from simple ones, for example fraction (numerator + denominator), complex numbers (real & imaginary part). OOP concepts: object, method. File management. Command-line arguments. Recursion (painting of an area, building a labyrinth). Algorithms efficiency, quick sorting, binary search versus linear search, O(n). Data structures: binary tree (algorithms), effectiveness: search trees (Morse tree). Mathematical libraries. Modules.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMEGT35A410 Accounting 2 0 0 f 3     3      
Course coordinator: Dr. Ágnes Laáb
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE92AM38 Analysis 1 3 2 0 v 7     7      
Course coordinator: Dr. Attila Andai
Preliminary requirement: Calculus 1
Descripton: Metrics and metric spaces. Topology of metric spaces. Basic properties of metric and normed spaces. Metric subspaces and isometrics. Sequences in metric spaces. Convergence of sequences in metric spaces. Separable metric spaces. Convergent sequences in normed spaces. Product of metric and normed spaces. Compact sets, relative compact sets and their its basic properties in metric spaces.  Characterization of compact metric spaces. Cantor's intersection theorem. Bolzano-Weierstrass theorem. Product of compact metric spaces. Equivalence of norms in finite-dimensional vector spaces. Limit of functions in metric spaces. Definition of continuity in terms of epsilon-delta and limits, and their equivalence. Topological characterization of continuity. Homeomorphism. Uniform continuity. Basic properties of continuous functions on compact spaces. Weierstrass's maximum-minimum principle. Characterization of compact sets in finite-dimensional normed spaces. Fundamental theorem of algebra. Approximation by Bernstein polynomials. Complete metric spaces. Contractions and Banach fixed point theorem in metric spaces. Totally bounded metric spaces and the Hausdorff characterisation theorem. Completeness of finite-dimensional normed spaces. Connected and path-connected metric spaces. Nowhere dense sets and Baire's category theorem. Banach spaces. Characterization of Banach spaces with absolutely convergent series. Linear and multi-linear maps between normed spaces and their continuity and norm. The normed space of linear and multi-linear maps between normed spaces. Positive, negative, definite and indefinite multi-linear maps. Bounded linear operators and functionals. Hahn-Banach theorem and some consequences. Banach-Steinhaus theorem. Open mapping theorem. Closed graph theorem. Bounded inverse theorem. Derivation of functions between normed spaces.
Literature:
– T. Tao: Analysis II.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM38 Algebra 1 3 2 0 v 7     7      
Course coordinator: Dr. Alex Küronya
Preliminary requirement: Introduction to Algebra 2
Descripton: Groups, semigroups. Basic properties of groups, group homomorphism, subgroups, cosets. Langrange's Theorem. Examples: diherdral groups, quaternion group, symmetric groups, alternating groups. Decomposition of permutations into disjoint cycles, transpositions. Permutation groups, group actions, transitivity, Cayley's Theorem. Cyclic groups, order of a group element. Cauchy's Theorem. Direct product of groups. Normal subgroups, factor group, Homomorphism Theorem, Noether's Isomorphism Theorems. Important subgroups: derived subgroup, centre, class equation. Subgroup chains, Sylow's Theorems, description of the structure of groups of small size. Nilpotent groups. Fundamental Theorem of Finite Abelian Groups. Free groups. Free algebras over rings, ideals, maximal and prime ideals. Description of the polynomial ring R[x]. Principal ideal domains. Noether rings, unique factorization domains (UFD). Factor rings, field extensions, construction of finite fields. Modules over rings, submodules, module homomorphisms. Semisimple  modules and rings. The structure of matrix algebras over division rings. Vector space and module constructions: factor module, direct product, direct sum, tensor product. Linear fuction and  the dual space.
Literature:
– P.J. Cameron: Introduction to Algebra, Oxford Science Publications, 1998.
– Atiyah, Macdonald: Introduction to commutative algebra, online textbook.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE95AM29 Probability Theory 1 2 2 0 v 6     6      
Course coordinator: Dr. Péter Bálint
Preliminary requirements: Calculus 2  AND  Combinatorics and Graph Theory 1
Descripton: Introduction: empirical background, sample space, events, probability as a set function. Enumeration problems, inclusion-exclusion formula, urn models, problems of geometric origin. Conditional probability: basic concepts, multiplication rule, law of total probability, Bayes formula, applications. Independence. Discrete random variables: probability mass function, Bernoulli, geometric, binomial, hypergeometric and negative binomial distributions. Poisson approximation of the binomial distribution, Poisson distribution, Poisson process, applications. General theory of random variables: (cumulative) distribution function and its properties, singular continuous distributions, absolutely continuous distributions and probability density functions. Important continuous distributions: uniform, exponential, normal (Gauss), Cauchy. Distribution of a function of a random variable, transformation of probability densities. Quantities associated to distributions: expected value, moments, median, variance and their properties. Computation for the important distributions. Steiner formula. Applications. Joint distributions: joint distibution, mass and density functions, marginal and conditional distributions. Important joint distributions: polynomial, polyhypergoemetric, uniform and mutlidimensional normal distribution. Conditional distribution and density functions. Conditional expectation and prediction, conditional variance. Vector of expected values, Covariance matrix, Cauchy-Schwartz inequality, correlation. Indicator random variables. Weak Law of Large Numbers: Bernoull Law of Large Numbers, Markov and Chebyshev inequality. Weak Law of Large numbers in full generality. Application: Weierstrass approximation theorem. Normal approximation of binomial distribution: Stirling formula, de Moivre-Laplace theorem. Applications. Normal fluctuations. Central Limit Theorem.
Literature:
– Ross, Sheldon: A First Course in Probability, 8th Edition, Pearson Education International, 2010.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM46 Programming Exercises for Probability Theory 0 0 0 f 1     1      
Course coordinator: Dr. Ferenc Wettl
Preliminary requirements: Informatics 2  AND  Probability Theory 1 [parallel]
Descripton: The aim of the course is to maintain the students' programming skills through programming problems associated with the topics of Probability Theory course helping the understanding of the basic concepts of probability simulations of random events at the same time.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE93AM15 Differential Equations 1 2 2 0 v 6     6      
Course coordinator: Dr. Katalin Nagy
Preliminary requirements: Introduction to Algebra 2  AND  Calculus 2
Descripton: Ordinary differential equations. Explicitly solvable equations, exact and linear equations. Well-posedness of the initial value problem, existence, uniqueness, continuous dependence on initial values. Approximate solution methods. Linear systems of equations, variational system.  Elements of stability theory, stability, asymptotic stability, Lyapunov functions, stability by the linear approximation. Phase portraits of planar autonomous equations. Laplace transform, application to solve differential equations. Discrete-time dynamical systems.
Literature:
– W.E. Boyce, R.C. DiPrima: Elementary Differential Equations and Boundary Value Problems, Wiley 2008.
– J.C. Robinson: An Introduction to Ordinary Differential Equations, Cambridge University Press 2003.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM44 Informaitcs 3 2 0 2 f 4       4    
Course coordinator: Dr. Alex Küronya
Preliminary requirements: Informatics 2  AND  Programming Exercises for Probability Theory
Descripton: The aim of the course is to understand the basic elements of C++ language fundamental in effective scientific calculations. Compiling C++ programs, programming environments for C++. Input/Output. Built-in data types: int, double, char, bool, complex. Control commands: if, switch, for, while, do. Exception handling (recall Python). Functions. Extending operators (fractions struct), references (a += b, cout << fraction, cin >> fractions). Object-oriented programming in C++: object, class, encapsulation, member functions, constructors, destructors (in complex class with re + im or r + fi data members). Using arrays in C++.  Pointers, relationship with arrays. File management. Basic algorithms: search, sort, etc.  Command-line arguments. Dynamic memory management, new[], delete[]. Inheritance. Templates. Libraries. Header files.
Literature:
– E. Scheinerman: C++ for Mathematicians. An Introduction for Students and Professionals, CRC Press.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE95AM31 Mathematical Statistics 1 2 0 2 v 5       5    
Course coordinator: Dr. Marianna Bolla
Preliminary requirement: Probability Theory 1
Descripton: Statistical sample, descriptive statistics, empirical distributions.Most frequently used probabilictic models, likelihood function, sufficiency, maximum likelihood principle. Theory of  point estimation: unbiased and asymptotically unbiased estimators, efficiency, consistency. Methods of point estimation: maximum likelihood, method of moments, Bayes principle. Interval estimation, confidence intervals. Theory of hypothesis testing, likelihood ratios. Parametric inference: u, t, F tests, comparing two treatments. Two-way classified data, contingency tables, chi-square test. Nonparametric inference: Wilcoxon and sign tests, Spearman correlation. Regression analysis. Linear regression, method of least squares, Pearson correlation. Multivariate regression, multiple correlation. Linear models, analysis of variance for one- and two-way classified data. Practical considerations: selecting the sample size, test for normality, resampling methods.
Literature:
– R.A. Johnson, G.K. Bhattacharyya: Statistics: Principles and methods, Wiley, New York, 1992.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE92AM39 Analysis 2 2 2 0 v 5       5    
Course coordinator: Dr. Attila Andai
Preliminary requirement: Analysis 1
Descripton: Ring, σ-ring, and σ-algebra of sets. Set functions. Concept of Lebesgue measure. Outer measure. Measurable sets.  Measure generated by an outer measure. Example for not Lebesgue-measurable set. Measure space, measurable functions. Null sets. The concept of convergence in measure and almost everywhere (ae) and relations between them. Integral of measurable functions. Beppo-Levi theorem, Fatou's lemma, Lebesgue's dominated convergence theorem. Lp-spaces, and Hölder and Minkowski inequality. Absolute continuity of the integral. Riemann sphere. Limits and properties of complex valued sequences. Limit and continuity of complex functions. Power series of elementary functions. Euler's formula. Complex logarithm function. Differentiability of complex functions. Cauchy-Riemann equations. Regularity of complex functions and elementary properties of regular functions. Harmonic functions, harmonic conjugate. Complex integral, integration by substitution. Newton-Leibniz formula. Goursat lemma. Cauchy's integral theorem and integral formula on convex domain. Index of a curve. Simply-connected subsets. Cauchy integral theorem and integral formula. Primitive functions. Morera's theorem. Power series of regular functions. Liouville theorem and fundamental theorem of algebra. Multiplicity of roots. Laurent series. Isolated, removable and essential singularities of complex functions. Laurent series. Concept of residue and the residue theorem. Residue theorem with logarithmic functions.  Argument principle. Rouche's theorem. Open mapping theorem. Maximum and minimum principles.
Literature:
– W. Rudin: Real and complex analysis.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE94AM19 Differential Geometry 1 2 1 0 f 4       4    
Course coordinator: Dr. Brigitta Szilágyi
Preliminary requirements: Geometry  AND  Calculus 2
Descripton: Definition of curve, parametrisation, reparametrisation, length and arclength, invariance of length under isometries, tangent vector, curvature, Fox-Milnor's theorem, normal vector, signed curvature and turning angle, total curvature and convexity, the four vertex theorem, isoperimetric inequality, Frenet-Serret frame, torsion, fundamental theorem of curves. Definition of a regular embedded surface, Gaussian curvature, principal curvatures, intrinsic geometry, Theorema Egregium, Christoffel symbols, PMC equations, fundamental theorem of surfaces, covariant derivative, Lie bracket, Riemann curvature tensor, geodesic curvature, geodesics, Gauss-Bonnet theorem.
Literature:
– M. Do Carmo: Differential Geometry of Curves and Surfaces.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE93AM19 Operations Research 2 2 0 v 5       5    
Course coordinator: Dr. Marianna Eisenberg-Nagy
Preliminary requirements: Introduction to Algebra 2 AND  Calculus 2
Descripton: Introduction to operations research; convex sets, polyhedron, polytope Krein-Milmann theorem. Separation, Farkas' lemma. Linear programming problem, basis, basic solution, optimal solution. Simplex algorithm. Two-phase simplex algorithm, degeneration, index selection rules. Modified simplex algorithm. Sensitivity testing. Weak and strong duality theorem. Network flow problems, algorithms. Network simplex algorithm. Transportation problem, assignment problem, the Hungarian method. Integer programming: Branch and bound method, dynamic programming, cutting plane procedures. Game theory: matrix games.
Literature:
– K.G. Murty: Linear and combinatorial programming, John Wiley and Sons., New York, 1976.
– V. Chvatal: Linear programming, W.H. Freeman & Co Ltd, 1983.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMEVISZAB01 Theory of Algorithms 2 2 0 v 4       4    
Course coordinator: Dr. Katalin Friedl
Preliminary requirements: Combinatorics and Graph Theory 1  AND  Informatics 2  AND  Programming Exercises for Theory of Algorithms [parallel]
Descripton: Pattern matching: naive algorithm, the fingerprinting method of Rabin and Karp, solution by finite automata. Deterministic and non-deterministic finite automata and their equivalence. Regular expressions, regular languages, and their connections to finite automata. Finite automaton as lexical analyser. Context free grammars. Parse tree, left and right derivation. Ambiguous words, grammars, languages. The importance of unambiguous grammars for algorithms. Pushdown automaton. Connection between pushdown automata and context free grammars, how to get a PDA from a CF grammar. The main task of a parser. The general automaton: Turing machine.  Church-Turing thesis. The classes  P, NP, coNP, their relations. Karp reduction and the notion of NP completeness. Theorem of Cook and Levin. 3SAT, 3COLOR are NP complete languages. Further NP complete languages: MAXSTABLE, HAM-CYCLE, HAM-PATH, TSP,  3DH, SUBSETSUM, PARTITION, KNAPSACK, SUBGRAPHISO. The problem of GRAPHISO.  Linear and integer programming. LP is in P (without proof), IP is in NP. LP and IP as algorithmic tools, translation of combinatorial problems to integer programming. Another tool: branch and bound. Dynamical programming (example: knapsack, longest common substring). The objective in approximation algorithms. Bin packing has fast and good approximations (FF, FFD, theorem of Ibarra and Kim). Fro the TSP even the approximation s hard in general but there is efficient 2-approximation in the euclidean case. Comparison based sorting: bubble sort, insertion sort, merge sort, quick sort. Lower bound for the number of comparisons. Other sorting methods: counting sort, bin sort, radix sort. Linear and binary search. The binary search is optimal in the number of comparisons. Notion of search tree, their properties and analysis. Red-black tree as a balanced search tree. The 2-3 tree, and its generalization, the B tree. Comparisons of the different data structures.
Literature:
– T. Corman, C. Leiserson, R. Rivest, C. Stein: Introduction to Algorithms, MIT Press.
– M. Sipser: Introduction to the Theory of Computing, Thomson.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM47 Programming Exercises for Theory of Algorithms 0 0 0 f 1       1    
Course coordinator: Dr. Ferenc Wettl
Preliminary requirements: Informatics 2  AND  Theory of Algorithms [parallel]
Descripton: The aim of the course is to maintain the students' programming skills through programming problems associated with the topics of Algorithm Theory course helping the understanding of the basic concepts of algorithms.
Literature:
– M.L. Hetland: Python Algorithms, Mastering Basic Algorithms in the Python Language, Apress, 2010.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE91AM39 Algebra 2 4 0 0 v 4         4  
Course coordinator: Dr. Erzsébet Lukács
Preliminary requirement: Algebra 1
Descripton: Field extensions, construction and uniqueness of simple algebraic extensions, finite and algebraic extensions. Normal extensions, splitting field, separable extension, finite fields, Wedderburn's theorem, Galois group, irreducibility of the cyclotomic polynomials, Galois groups of radical extensions, Galois correspondence, Fundamental theorem of Galois theory. Applications of Galois theory: Fundamental theorem of algebra, ruler and compass constructions, solvability of equations by radicals, Abel–Ruffini theorem. Existence and uniqueness of algebraic closure, transcendental extensions, transcendence of e, Gelfand-Schneider theorem. - Review of the basic concepts of number theory, Euler ϕ function. Linear congruences and systems of congruences, binomial congruences of higher degree, discrete logarithm, congruences of prime power moduli. Quadratic congruences, Legendre and Jacobi symbol, quadratic reciprocity. Prime numbers: Euclid's theorem, gaps between primes, Chebyshev's theorem, harmonic series of primes, Dirichlet's theorem for (nk + 1). Arithmetic functions: d(n), σ(n), ϕ(n). Multiplicativity, convolution, Möbius function, the Möbius inversion formula. Prime number theorem, magnitude of the nth prime, prime tests, Rabin–Miller test, RSA function. Diophantine equations: linear diophantine equations, Pythagorean triples, Fermat's two squares theorem, Gaussian integers.
Literature:
– I. Stewart: Galois Theory, CRC Press, 2003.
– Niven, Zuckerman, Montgomery: An Introduction to the Theory of Numbers, John Wiley & Sons, 1960.
– M.B. Nathanson: Elementary Methods in Number Theory, Springer, 2000.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE93AM16 Optimization Models 2 0 2 f 4         4  
Course coordinator: Dr. Boglárka Gazdag-Tóth
Preliminary requirements: Calculus 2  AND  Informatics 1  AND  Operations Research
Descripton: Introduction to mathematical modeling, to mathematical programming problems, and their classification. Model reformulations: rewrite complex transportation problem to simple transportation problem, rewrite maximum flow problem to minimum cost maximal flow problem. Modeling problems in economy. Integer modeling tricks, set covering, set partitioning problems. Modeling Facility Location problems. Numerical errors. Dynamic programming. Scheduling problems, heuristics, approximations, online versions. Decision Theory. Inventory tasks.
Literature:
– R. Fourer, D.M. Gay, B.W. Kernighan: AMPL: A Modeling Language for Mathematical Programming, Second edition.
– L. Liberti: Problems and Exercises in Operations Research, École Polytechnique, 2007.
– W.L. Winston: Operations Research: Applications and Algorithms. 4th edition, Thomson Learning. 
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE95AM34 Stochastic Processes 5 0 0 v 6         6  
Course coordinator: Dr. Károly Simon
Preliminary requirement: Probability Theory 1
Descripton: Basic notions: finite dimensional marginals, Kolmogorov’s fundamental theorem, strongly and weakly stationary processes, processes with stationary and/or independent increments. Discrete Markov chains: linear algebra of stochastic matrices, classification of states. Finite Markov chains: stationary measures and ergodic behaviour. Reversibility, random walk on graphs. Urn models. Countable Markov chains: transience, null-recurrence, positive-recurrence. Random walks on Zd: Polya’s theorem. Random walks on countable graphs, branching processes, discrete time birth-and-death processes, queuing problems. Random walks on Z1: the reflection principle and limit distribution of the maximum, difference equations. Continuous time, discrete space Markov processes: the Poisson process, jump rates, exponential clocks. Stochastic semigroup: Kolmogorov-Chapman equations, infinitesimal generator. Complements of measure theory: filtrations, adapted processes, natural filtration. The general notion of conditional expectation (Kolmogorov’s theorem), fundamental properties. Discrete time martingales: sub/super/martingales, stopping times, stopped martingales. Optional stopping theorem, Wald identity, martingale convergence theorem, submartingale inequality, maximal inequality. Azuma-Hoffding inequality, applications. The Brownian motion: defining properties, covariances. Sketch of Paul Levy’s construction, basic analytic properties. Applications.
Literature:
– Essentials of Stochastic Processes (2nd edition), Springer, 2012.
– R. Durrett: Probability Theory with Examples, 4th edition, Cambridge U. Press, 2010.
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BMETE95AM12 Creating Mathematical Models 0 2 0 f 2         2  
Course coordinator: Dr. Domokos Szász
Preliminary requirements: Calculus 2  AND  Algebra 1
Descripton: The aim of the seminar to present case studies on results, methods and problems from applied mathematics for promoting. The spreading of knowledge and culture of applied mathematics. The development of the connections and cooperation of students and professors of the Mathematical Institute, on the one hand, and of personal, researchers of other departments of the university or of other firms, interested in the applications of mathematics. The speakers talk about problems arising in their work. They are either applied mathematicians or non-mathematicians, during whose work the mathematical problems arise. An additional aim of this course to make it possible for interested students to get involved in the works presented for also promoting their long-range carrier by building contacts that can lead for finding appropriate jobs after finishing the university.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMEGT30A410 Micro- and Macroeconomics 3 0 0 f 4         4  
Course coordinator: Dr. Katalin Petró
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE92AMxx Applied Numerical Methods with Matlab 2 0 2 f 4           4
Course coordinator: Dr. Róbert Horváth
Descripton: Usage of MATLAB (all discussed numerical methods will be introduced and tested in MATLAB ). The discussed topics are:  error calculation,  direct and iterative solution of linear systems of equations: Gauss elimination, Gauss transform factorizations of matrices, conditionality of linear systems of equations, Jacobi, Seidel and SOR iteration; convergence of the iteration, error estimation, optimization type methods for solving linear systems of equations, estimation of the eigenvalue, power method for the eigenvalue, eigenvector problem of matrices, inverse power method, transforming matrices to special forms, Jacobi method for determining eigenvalues and eigenvectors, QR method for determining eigenvalues, simple interpolation with polynomials, Hermite interpolation,  interpolation with third degree spline, approximation according to least squares with polynomials and trigonometric polynomials,  trigonometric interpolation, basics of fast Fourier transform, numerical integration, Newton-Cotes formula and its usage, Gaussian quadrature, solution of non linear systems of equations, roots of polynomials, numerical solution to the initial value problems of ordinary differential equations, basic terms of one step methods, Runge-Kutta methods, stability, convergence and error estimation of one step methods, multi step methods.
Literature:
– S.C. Chapra: Applied Numerical Methods with MATLAB - for engineers and scientists, McGraw Hill, 2008.
– W. Cheney, D. Kincaid, Numerical Mathematics and Computing, Brooks/Cole, Cangage learning, 2013.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE94AM20 Differential Geometry 2 3 1 0 v 4           4
Course coordinator: Dr. Szilárd Szabó
Preliminary requirement: Differential Geometry 1
Descripton: Differentiable manifolds, tangent space, tangent bundle. Integral curve of a vector field. Vector bundles and related algebraic constructions (direct sum, tensor product, dual, homomorphisms). Differential forms, pull-back, exterior product, exterior derivation. Integration on compact oriented manifolds, Stokes' theorem. Lie-derivative, Lie-Cartan formula. Riemannian metric, examples. Geodetics, exponential map. Lie groups and algebras. Hopf-Rinow theorem and its consequences. Connections on a vector bundle, parallel transport, integrability. Levi-Civita connection, the Riemann curvature tensor. Properties of the curvature tensor, Ricci curvature. First and second variation of arc length, Jacobi vector fields.
Literature:
– P. Petersen: Riemannian geometry, Graduate Texts in Mathematics, 171. Springer.
– S. Gallot, D. Hulin, J. Lafontaine: Riemannian geometry, Universitext, Berlin, Springer.
– M. Berger, B. Gostiaux: Differential geometry: manifolds, curves, and surfaces, Graduate Texts in Mathematics.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMEGT35A411 Finance 2 0 0 f 3           3
Course coordinator: Dr. Imre Tarafás
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE90AM47 BSc Thesis Project 0 0 10 f 10           10
Course coordinator: Dr. Miklós Horváth
Preliminary requirement: at least 144 credits
Descripton: This course is for graduate students to prepare their graduate thesis in which they prove that they can use the acquired knowledge independently and creatively.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE95AM33 Tools of Modern Probability Theory 4 0 0 v 4         4  
Course coordinator: Imre Péter Tóth
Preliminary requirement: Probability Theory 1
Descripton: The goal of the course is to teach the most important tools that modern probability theory uses from combinatorics, linear algebra, real analysis, measure theory, complex analysis, functional analysis and geometry. We demonstrate the use of these tools through examples, but the emphasis is on developing the tools. A part of the knowledge acquired will be utilised in the masters program. Combinatorics: method of generator functions. Stirling formula. Euler gamma function. Topology: convergence on metric spaces and topological spaces. Compactness. Product space, product topology. Tychonoff's theorem. Linear algebra: inner product spaces. Cauchy-Schwartz inequality. Calculating powers of matrices, analytic matrix-calculus. (Application: Markov transition probabilities.) Transformations of functions: Laplace transform. Fourier expansion, Fourier transformation. Discrete Fourier tranformation. (Application: characteristic function.) Legendre transform. Measure theory: exchanging integral and derivative. Uniform convergence and continuity. (Application: differentiability of the characteristic function.) Jensen inequality. Absoulte continuity, Radon-Nikodym theorem. (Application: conditional expectation.) Push-forward of measures, integration by substitution. (Application: distribution of random variables, expectation of random variables.) Product space, product measure. Fubini's theorem. (Application: independence.) Decomposition of measures, conditional measure, factor measure. Complex analysis: Residue theorem, Laurent expansion. (Application: calculating convolutions and characteristic functions.) Analytic extensin, Vitali's theorem. Functional analysis: spectrum of bounded operators, resolvent, spectral radius. Hahn-Banach theorem. C^k spaces, Arsela-Ascoli theorem. Continuous linear functionals, Riesz-Markov theorem. Dual spaces, weak star topology, tightness. Fourier transform once again, Riesz-Fischer theorem.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE92AM42 Measure Theory 4 0 0 v 4         4  
Course coordinator: Dr. Miklós Horváth
Preliminary requirement: Analysis 2
Descripton: Recapitulation: sigma-algebra, outer measure, measure. Signed measure, Hahn decomposition. Radon measures, approximation theorem. Lebesgue-Stieltjes measure. Measurable functions. Convergence in measure. Theorems of Egoroff and Lusin. Integration in measure spaces. Absolute continuity of the integral. Integration of sequences of functions: theorems of Beppo-Levi, Fatou and Lebesgue. Products of measure spaces, Fubini theorem. Lp spaces. Absolutely continuous and singular measures, Radon-Nikodym derivative, Lebesgue decomposition. Absolutely continuous functions, Newton-Leibniz formula. Total variation. Functions of bounded variation, decomposition into absolutely continuous and singular parts.
Literature:
– T. Tao: An introduction to measure theory, http://terrytao.files.wordpress.com/2011/01/measure-book1.pdf
– P.R. Halmos: Measure Theory, Springer 1978.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE90AM48 Individual Research Project 1 0 0 0 f 2         2  
Course coordinator: Dr. Miklós Horváth
Descripton: Under the guidance of a chosen tutor, the student works on understanding a paper or a book chapter about contemporary mathematics. The goal is to get familiar with basic methods and abilities of research like exact understanding of mathematics in English, use of libraries and of the net etc. At the end of the semester the student makes a written English summary in a few pages and gives a short presentation in a seminar talk.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE92AM45 Partial Differential Equations 2 2 0 v 4           4
Course coordinator: Dr. János Karátson
Preliminary requirements: Differential Equations 1  AND  Analysis 2
Descripton: Classification of partial differential equations (PDEs). First order linear PDEs. Convection transport processes. First order quasilinear PDEs. Parabolic Cauchy problems. Heat conduction problem, qualitative properties. Hyperbolic Cauchy problems. Wave equation in one space dimension: vibrating string, travelling and standing waves. Wave equation in two and three space dimensions using surface integral. Elliptic boundary value problems. Elliptic models: stationary heat distribution, elastic torsion. Uniqueness of the solution. The problem of the notion of solution. Theoretical background: Hilbert spaces, Fourier series, symmetric operators. Fourier series expansion for elliptic boundary value problems using eigenfuctions. Theoretical background: distributions, Sobolev spaces. Weak solution of elliptic problems. Weak eigenvalue problem. Parabolic and és hyperbolic initial-boundary value problems. Elliptic fundamental solution, mathematical description of the potential for a point source, Green’s function.
Literature:
– L.C. Evans: Partial Differential Equations, AMS, 2010.
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BMETE94AM22 Convex Geometry 2 2 0 v 4           4
Course coordinator: Dr. Zsolt Lángi
Preliminary requirements: Geometry  AND  Introduction to Algebra 2
Descripton: Introduction: affine and convex sets, affine dependence, independence, affine and convex combinations, affine hull, isolation theorem, characterization of closed, convex sets as the intersection of closed half spaces. Convex hull, theorems of Radon, Helly and Carathéodory, their applications. Linear functionals and their connection with hyperplanes, Minkowski sum, separation of convex sets with hyperplanes, supporting hyperplanes, faces of a convex body, extremal and exposed points, theorems of Krein-Milman and Straszewicz. Indicator function, algebras of closed/compact convex sets, valuations, Euler characteristic and the proof of its existence. Convex polytopes and polyhedral sets, their connection, face structure of polytopes, combinatorial equivalence. The f-vector of polytopes, Euler characteristic of polytopes, theorem of Euler. Polar of a set, fundamental properties of polarity, properties of the polar of a polytope, dual polytope. Moment curve, cyclic polytopes and their face structure, Gale’s evenness condition. Hausdorff distance of convex bodies. Affine transformations, Banach-Mazur distance. Ellipsoid as an affine ball. Unique existence of largest volume inscribed, and smallest volume circumscribed ellipsoid of a convex body. The Löwner-John ellipsoid, John’s theorem for general, and centrally symmetric convex bodies.
Literature:
– B. Grünbaum: Convex Polytopes, Graduate Texts in Mathematics 221, Springer, New York, 2003.
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BMEVISZA026 Combinatorics and Graph Theory 2 2 2 0 v 4           4
Course coordinator: Dr. Tamás Fleiner
Preliminary requirement: Combinatorics and Graph Theory 1
Descripton: Geometric and abstract duality, weak isomorphism (2-isomorphism) and the Whitney theorems. Vertex and edge coloring, Mycielsky's construction, Brooks' theorem. 5-colour theorem, Vizing's theorem, connection of edge-colouring to matchings, Petersen's theorem. List colouring of graphs, Galvin's theorem. Perfect graphs, interval graphs and the perfect graph theorem. Ramsey's theorem, Erdős-Szekeres theorem, Erdős' lower bound and the probabilistic method. Turán's theorem, Erdős-Stone theorem, Erdős-Simonovits theorem. Hypergraphs, Erdős-Ko-Rado theorem, Sperner's theorem and the LYM inequality. De Bruijn-Erdős theorem, finite planes, construction from finite field, and from difference sets. Generating functions, Fibonacci numbers, Catalan numbers. Posets, Dilworth's theorem.
Literature:
– R. Diestel: Graph Theory, online available.
– J.A. Bondy, U.S.R. Murty: Graph Theory with Applications.
Code Title Lc Pr Lb Rq Cr I II III IV V VI
BMETE90AM49 Individual Research Project 2 0 0 0 f 2           2
Course coordinator: Dr. Miklós Horváth
Descripton: Under the guidance of a chosen tutor, the student works on understanding a paper or a book chapter about contemporary mathematics. The goal is to get familiar with basic methods and abilities of research like exact understanding of mathematics in English, use of libraries and of the net etc. At the end of the semester the student makes a written English summary in a few pages and gives a short presentation in a seminar talk.