Mathematical Institute
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- Lecturer: Benjamin Hambly
The course surveys the basics of stochastic calculus as a preparation for developments in the Michaelmas term courses
Prerequisites
It will be assumed that students have a good understanding of probability and measure and at least a first course in stochastic processes
- Lecturer: Kathryn Gillow
- Lecturer: Katia Babbar
- Lecturer: Anran Hu
The course aims to cover the following topics:
- Model Diagnostic and Model Selection (AIC/BIC).
- Non-parametric Regression and splines
- Lasso/Ridge/Elastic Net
- Kernel Regression
- Transformations, weighted regression and heteroskedasticity.
- Principal Component Analysis (PCA)
- Logistic Regression
- Decision Trees and Random Forest
- Support Vector Machine
- Time Series Models: AR(p), MA(q)
- Time Series Models: ARMA(p,q) models
- ARIMA, models with trends and seasonality
- GARCH models
The theory of functions of a complex variable is a rewarding branch of mathematics to study at the undergraduate level with a good balance between general theory and examples. It occupies a central position in mathematics with links to analysis, algebra, number theory, potential theory, geometry, topology, and generates a number of powerful techniques (for example, evaluation of integrals) with applications in many aspects of both pure and applied mathematics, and other disciplines, particularly the physical sciences.
In these lectures we begin by introducing students to the language of topology before using it in the exposition of the theory of (holomorphic) functions of a complex variable.
The central aim of the lectures is to present Cauchy's Theorem and its consequences, particularly series expansions of holomorphic functions, the calculus of residues and its applications.
The course concludes with an account of the conformal properties of holomorphic functions and applications to mapping regions.
Prof. Ben Green
Prof. Panagiotis Papazoglou
Students will have been introduced to point-set topology and will know the central importance of complex variables in analysis. They will have grasped a deeper understanding of differentiation and integration in this setting and will know the tools and results of complex analysis including Cauchy's Theorem, Cauchy's integral formula, Liouville's Theorem, Laurent's expansion and the theory of residues.
Metric Spaces (10 lectures)
Basic definitions: metric spaces, isometries, continuous functions (\(\varepsilon-\delta\) definition), homeomorphisms, open sets, closed sets. Examples of metric spaces, including metrics derived from a norm on a real vector space, particularly \(l^1, l^2, l^\infty\) norms on \(\mathbb{R}^n\), the sup norm on the bounded real-valued functions on a set, and on the bounded continuous real-valued functions on a metric space. The characterisation of continuity in terms of the pre-image of open sets or closed sets. The limit of a sequence of points in a metric space. A subset of a metric space inherits a metric. Discussion of open and closed sets in subspaces. The closure of a subset of a metric space. [3]
Completeness (but not completion). Completeness of the space of bounded real-valued functions on a set, equipped with the norm, and the completeness of the space of bounded continuous real-valued functions on a metric space, equipped with the metric. Lipschitz maps and contractions. Contraction Mapping Theorem. [2.5]
Connected metric spaces, path-connectedness. Closure of a connected space is connected, union of connected sets is connected if there is a non-empty intersection, continuous image of a connected space is connected. Path-connectedness implies connectedness. Connected open subset of a normed vector space is path-connected. [2]
Definition of sequential compactness and proof of basic properties of compact sets. Preservation of compactness under continuous maps, equivalence of continuity and uniform continuity for functions on a compact set. Equivalence of sequential compactness with being complete and totally bounded. The Arzela-Ascoli theorem (proof non-examinable). Open cover definition of compactness. Heine-Borel (for the interval only) and proof that compactness implies sequential compactness (statement of the converse only). [2.5]
Complex Analysis (22 lectures)
Basic geometry and topology of the complex plane, including the equations of lines and circles. Extended complex plane, Riemann sphere, stereographic projection. Möbius transformations acting on the extended complex plane. Möbius transformations take circlines to circlines. [3]
Complex differentiation. Holomorphic functions. Cauchy-Riemann equations (including \(z,\bar{z}\) version). Real and imaginary parts of a holomorphic function are harmonic. [2]
Recap on power series and differentiation of power series. Exponential function and logarithm function. Fractional powers — examples of multifunctions. The use of cuts as method of defining a branch of a multifunction. [3]
Path integration. Cauchy's Theorem. (Sketch of proof only — students referred to various texts for proof.) Fundamental Theorem of Calculus in the path integral/holomorphic situation. [2]
Cauchy's Integral formulae. Taylor expansion. Liouville's Theorem. Identity Theorem. Morera's Theorem. [4]
Laurent's expansion. Classification of isolated singularities. Calculation of principal parts, particularly residues. [2]
Residue Theorem. Evaluation of integrals by the method of residues (straightforward examples only but to include the use of Jordan's Lemma and simple poles on contour of integration). [3]
Conformal mappings. Riemann mapping theorem (no proof), Möbius transformations, exponential functions, fractional powers; mapping regions (not Christoffel transformations or Joukowski's transformation). [3]
Scientific computing pervades our lives: modern buildings and structures are designed using it, medical images are reconstructed for doctors using it, the cars and planes we travel on are designed with it, the pricing of "Instruments'' in the financial market is done using it, tomorrow's weather is predicted with it. The derivation and study of the core, underpinning algorithms for this vast range of applications defines the subject of Numerical Analysis. This course gives an introduction to that subject. It covers the basics of three key fields in the subject: Numerical Linear Algebra, Approximation Theory, and Numerical Solution of Differential Equations.
Through studying the material of this course students should gain an understanding of numerical methods, their derivation, analysis and applicability. They should be able to solve certain mathematically posed problems using numerical algorithms. This course is designed to introduce numerical methods - i.e. techniques which lead to the (approximate) solution of mathematical problems which are usually implemented on computers. The course covers derivation of useful methods and analysis of their accuracy and applicability.
The course begins with a study of methods and errors associated with approximation of functions which are described by data values (interpolation or data fitting). Following this we turn to numerical methods of linear algebra, which form the basis of a large part of computational mathematics, science, and engineering. Key ideas here include algorithms for linear equations, least squares, and eigenvalues built on LU and QR matrix factorizations. We also treat the singular value decomposition (SVD), a fundamental matrix decomposition of major importance in data science.
We then return to approximation of functions, and discuss key concepts in orthogonal polynomials and best L2 polynomial approximation, and cover the beautiful method of Gauss quadrature for numerical integration.
The final part of this course treats the numerical solution of initial value problems for ordinary differential equations (ODEs), which directly complements the theoretical study of these problems in Part A Differential Equations I. We discuss basic and advanced methods for their numerical solution, the fundamental concept of numerical stability, and culminate with Dahlquist’s theorem that consistency and stability imply convergence.
The course requires elementary knowledge of functions, calculus, linear algebra and ordinary differential equations.
Although there are no assessed practicals for this course, the tutorial work involves a mix of written work and computational experiments. Knowledge of a programming language such as Matlab or Python would be desirable, but many examples will be provided.
Prof. Yuji Nakatsukasa
At the end of the course the student will know how to:
Find the solution of linear systems of equations.
Compute eigenvalues and eigenvectors of matrices.
Compress matrices vie the SVD.
Approximate functions of one variable by polynomials and piecewise polynomials (splines).
Compute good approximations to one-dimensional integrals.
Numerically solve initial value problems for ODEs.
Understand the stability of the numerical methods employed.
Use computing to achieve these goals.
Lagrange interpolation [1 lecture]
Gaussian elimination, LU, QR factorisations, least-squares problems [3.5 lectures]
Eigenvalues: Gershgorin’s Theorem, symmetric QR algorithm, polynomial rootfinding via eigenvalues [3.5 lectures]
SVD and low-rank matrix approximation [2 lectures]
Best approximation in inner product spaces, orthogonal polynomials, Gauss quadrature [3 lectures]
Forward and backward Euler, trapezium rule, leapfrog, Runge-Kutta methods [3 lectures]
Linear multi-step methods and Dahlquist’s theorem [2 lectures]
Part A Integration is essential; the only concepts which will be used are the convergence theorems and the theorems of Fubini and Tonelli, and the notions of measurable functions, integrable functions, null sets and L^p spaces. No knowledge is needed of outer measure, or of any particular construction of the integral, or of any proofs. A good working knowledge of Part A Core Analysis (both metric spaces and complex analysis) is expected.
The course provides an introduction to the methods of functional analysis.
It builds on core material in analysis and linear algebra studied in Part A. The focus is on normed spaces and Banach spaces; a brief introduction to Hilbert spaces is included, but a systematic study of such spaces and their special features is deferred to B4.2 Functional Analysis II. The techniques and examples studied in the Part B courses Functional Analysis I and II support, in a variety of ways, many advanced courses, in particular in analysis and partial differential equations, as well as having applications in mathematical physics and other areas.
Prof. Melanie Rupflin
Students will have a firm knowledge of real and complex normed vector spaces, with their geometric and topological properties. They will be familiar with the notions of completeness, separability and density, will know the properties of a Banach space and important examples, and will be able to prove results relating to the Hahn-Banach Theorem. They will have developed an understanding of the theory of bounded linear operators on a Banach space.
Brief recall of material from Part A Metric Spaces and Part A Linear Algebra on real and complex normed vector spaces, their geometry and topology and simple examples of completeness. The norm associated with an inner product and its properties. Banach spaces, exemplified by \(\ell^p\), \(L^p\), \(C(K)\), spaces of differentiable functions. Finite-dimensional normed spaces, including equivalence of norms and completeness. Hilbert spaces as a class of Banach spaces having special properties (illustrations, but no proofs); examples (Euclidean spaces, \(\ell^2\), \(L^2\)).
Density. Approximation of functions, Stone-Weierstrass Theorem. Separable spaces; separability of subspaces.
Bounded linear operators, examples (including integral operators). Continuous linear functionals. Dual spaces. Hahn-Banach Theorem (proof for separable spaces only); applications, including density of subspaces and embedding of a normed space into its second dual. Adjoint operators.
Spectrum and resolvent. Spectral mapping theorem for polynomials.
B4.1 Functional Analysis I is an essential pre-requisite. A4 Integration is also essential; the only concepts which will be used are the convergence theorems and the theorems of Fubini and Tonelli, and the notions of measurable functions, integrable functions, null sets and L^p spaces. No knowledge is needed of outer measure, or of any particular construction of the integral, or of any proofs. A good working knowledge of Part A Core Analysis (both metric spaces and complex analysis) is expected.
The course provides further introduction to the methods of functional analysis. It builds on core material in Part A analysis and linear algebra and in Part B B4.1 Functional Analysis I. On one hand, it delves deeper into operator theory on Banach spaces, and on the other, it gives a systematic study of Hilbert spaces, operators on Hilbert spaces and their special features. The techniques and examples studied in the course, together with that in B4.1, support, in a variety of ways, many advanced courses, in particular in analysis and partial differential equations, as well as having applications in mathematical physics and other areas.''
Prof. Luc Nguyen
Students will appreciate the role of completeness through the Baire category theorem and its consequences for operators on Banach spaces. They will have a demonstrable knowledge of the properties of a Hilbert space, including orthogonal complements, orthonormal sets, complete orthonormal sets together with related identities and inequalities. They will be familiar with the theory of linear operators on a Hilbert space, including adjoint operators, self-adjoint and unitary operators with their spectra. They will know the \(L^2\)-theory of Fourier series and be aware of the classical theory of Fourier series and other orthogonal expansions.
Hilbert spaces; examples including \(L^2\)-spaces. Orthogonality, orthogonal complement, closed subspaces, projection theorem. Riesz Representation Theorem.
Linear operators on Hilbert space, adjoint operators. Self-adjoint operators, orthogonal projections, unitary operators.
Baire Category Theorem and its consequences for operators on Banach spaces (Uniform Boundedness, Open Mapping, Inverse Mapping and Closed Graph Theorems). Strong convergence of sequences of operators.
Weak convergence. Weak precompactness of the unit ball.
Spectral theory in Hilbert spaces, in particular spectra of self-adjoint and unitary operators.
Orthonormal sets, Pythagoras, Bessel's inequality. Complete orthonormal sets, Parseval. \(L^2\)-theory of Fourier series, including completeness of the trigonometric system. Examples of other orthogonal expansions (Legendre, Laguerre, Hermite etc.).
Brief contextual comments on the classical theory of Fourier series and modes of convergence; exposition of failure of pointwise convergence of Fourier series of some continuous functions.
Distribution Theory and Analysis of PDEs is a pre-requisite.
Distribution theory can be thought of as the completion of differential calculus, just as Lebesgue integration theory can be thought of as the completion of integral calculus. It was created by Laurent Schwartz in the 20th century, as was Lebesgue's integration theory.
Distribution theory is a powerful tool that works very well in conjunction with the theory of Fourier transforms. One of the main areas of applications is to the theory of partial differential equations. In this course we give an introduction to these three theories.
Prof. Jan Kristensen
Students will become acquainted with the basic techniques that in many situations form the starting point for the modern treatment of PDEs.
The Fourier transform on \(\mathbb{R}^n\): the Schwartz class \(\mathcal{S}\) of test functions on \(\mathbb{R}^n\) , properties of the Fourier transform on \(\mathcal{S}\), the Fourier transform of a Gaussian and the inversion formula on \(\mathcal{S}\). [4 lectures]
The class of tempered distributions \(\mathcal{S}'\) and their calculus. Fourier transforms of tempered distributions: definitions and examples, convolutions with tempered distributions. The inversion formula on \(\mathcal{S}'\). Fourier transfor in \(L^2\) and ~Plancherel's theorem. [5 lectures]
Solving PDEs using Fourier transformation: the Laplace equation, the heat equation, the wave equation, Schrödinger's equation. Fundamental solutions, ellipticity and hypoellipticity. [3 lectures]
Fourier Analysis: the Riemann-Lebesgue lemma, Paley-Wiener theorems, the Poisson summation formula, the uncertainty principle.[4 lectures]
Students wishing to take this course are expected to have a thorough understanding of the basic theory of normed vector spaces (including properties and standard examples of Banach and Hilbert spaces, dual spaces, and the Hahn-Banach theorem) and of bounded linear operators (ideally including the Open Mapping Theorem, the Inverse Mapping Theorem and the Closed Graph Theorem). Some fluency with topological notions such as (sequential) compactness and bases of topological spaces will also be assumed, as will be basic familiarity with the Lebesgue integral. A number of these prerequisites will be reviewed (briefly) during the course, and there will be a document available on the course webpage summarising most of the relevant background material.
This course builds on what is covered in introductory courses on Functional Analysis, by extending the theory of Banach spaces and operators. As well as developing general methods that are useful in operator theory, we shall look in more detail at the structure and special properties of "classical'' sequence spaces and function spaces.
Prof. Stuart White
By the end of this course, students will be able to:
- Establish and use both extension and separation versions of the Hahn Banach Theorem, and geometric properties of the norm, to obtain dualities between embeddings and quotients and establish reflexivity both abstractly and in important examples, such as Lebesgue spaces.
- work with the weak and weak*-topologies on Banach spaces, establish and use the Banach-Alaoglu theorem, relating this to characterisations of reflexivity, and describe closures in both norm and weaker topologies using annihilators, and preannhilators.
- Manipulate properties of compact and Fredholm operators on Banach and Hilbert spaces, to establish and use the Fredholm alternative, and obtain spectral theorems for compact operators both in abstract and concrete settings.
Normed vector spaces and Banach spaces. Dual spaces. Direct sums and complemented subspaces. Quotient spaces and quotient operators.
The Baire Category Theorem and its consequences (review).
Hahn-Banach extension and separation theorems. The bidual space. Reflexivity. Completion of a normed vector space.
Convexity and smoothness of norms. Lebesgue spaces and their duals.
Weak and weak* topologies. The Banach-Alaoglu theorem. Goldstine's theorem. Equivalence of reflexivity and weak compactness of the closed unit ball. The Schur property of ℓ1 Weakly compact operators.
Compactness in normed vector spaces. Compact operators. Schauder's theorem on compactness of dual operators. Completely continuous operators.
The Closed Range Theorem. Fredholm theory: Fredholm operators; the Fredholm index; perturbation results; the Fredholm Alternative. Spectral theory of compact operators. The Spectral Theorem for compact self-adjoint operators.
The only necessary prerequisite is a basic complex analysis course: analytic functions, Taylor series, contour integration, Cauchy theorems, residues, maximum modulus, Liouville's theorem. Some basic results that might not be covered by a basic course (such as argument principle and the Rouche theorem) will be given in the introductory chapter of the lecture notes.
The theory of conformal maps has a very long and rich history. Its foundations were laid out by Riemann in mid XIX century and it was one of the central areas of the Complex Analysis since then.
The aim of the course is to teach the principal techniques and methods of analytic and geometric function theory. We start by proving the Riemann mapping theorem which claims that all non-trivial simply connected domains are conformal images of the unit disc. This provides a connection between planar domains and 1-to-1, or univalent, analytic functions in the unit disc.
In this course we will study the Riemann mapping theorem and general theory of univalent functions, study how geometric properties of domains are related to analytic properties of functions. We will also study conformal invariants: quantities that do not change under conformal transformations. We will see that many of these invariants have a simple geometric interpretation and could be used to obtain numerous beautiful results.
Prof. Qian Wang
Students will have been introduced to ideas and techniques of geometric function theory that play important role and have a lot of applications in other areas of analysis. In particular, they will learn the proof of the Riemann mapping theorem and the concept of conformal invariants.
We will cover the following topics:
- The Riemann mapping theorem. The main goal will be to prove Riemann's theorem which tells us that any non-trivial simply-connected domain can be conformally mapped onto the unit disc. This will be the key result for the entire course since it will allow us to connect the geometry of a domain with the analytical properties of a conformal map which sends this domain to the unit disc. Within this section we will discuss
- Möbius transformations and the Schwarz lemma
- Normal families, the Montel and Hurwitz theorems
- Proof of Riemann mapping theorem
- Constructive uniformization: Christoffel-Schwarz mappings and iterative methods
- Boundary correspondence, accessible points and prime ends
- Uniformization of multiply-connected domains (without proof)
- Applications: the Dirichlet boundary problem
- Area theorem and coefficient estimates
- The Koebe \(1/4\) theorem, distortion theorems
- Conformal invariants: extremal length and its applications
- Conformal invariance of Green's function and harmonic measure
- Extremal length, its conformal invariance and applications
Integration and measure theory, martingales in discrete and continuous time, stochastic calculus. Functional analysis is useful but not essential.
Stochastic analysis and partial differential equations are intricately connected. This is exemplified by the celebrated deep connections between Brownian motion and the classical heat equation, but this is only a very special case of a general phenomenon. We explore some of these connections, illustrating the benefits to both analysis and probability.
Prof. Harald Oberhauser
The student will have developed an understanding of the deep connections between concepts from probability theory, especially diffusion processes and their transition semigroups, and partial differential equations.
Feller processes and semigroups. Resolvents and generators. Hille-Yosida Theorem (without proof). Diffusions and elliptic operators, convergence and approximation. Stochastic differential equations and martingale problems. Duality. Speed and scale for one dimensional diffusions.
Green's functions as occupation densities. The Dirichlet and Poisson problems. Feynman-Kac formula.
In these lectures we study the real and complex numbers, and study their properties, particularly completeness; define and study limits of sequences, convergence of series, and power series.
See the examinable syllabus.
Dr Vicky Neale
Students will have:
(i) an ability to work within an axiomatic framework;
(ii) a detailed understanding of how Cauchy's criterion for the convergence of real and complex sequences and series follows from the completeness axiom for \(\mathbb{R}\), and the ability to explain the steps in standard mathematical notation;
(iii) knowledge of some simple techniques for testing the convergence of sequences and series, and confidence in applying them;
(iv) familiarity with a variety of well-known sequences and series, with a developing intuition about the behaviour of new ones;
(v) an understanding of how the elementary functions can be defined by power series, with an ability to deduce some of their easier properties.
Real numbers: arithmetic, ordering, suprema, infima; the real numbers as a complete ordered field. Definition of a countable set. The countability of the rational numbers. The reals are uncountable. The complex number system. The triangle inequality.
Sequences of real or complex numbers. Definition of a limit of a sequence of numbers. Limits and inequalities. The algebra of limits. Order notation: \(O\), \(o\).
Subsequences; a proof that every subsequence of a convergent sequence converges to the same limit; bounded monotone sequences converge. Bolzano--Weierstrass Theorem. Cauchy's convergence criterion.
Series of real or complex numbers. Convergence of series. Simple examples to include geometric progressions and some power series. Absolute convergence, Comparison Test, Ratio Test, Integral Test. Alternating Series Test.
Power series, radius of convergence. Examples to include definition of and relationships between exponential, trigonometric functions and hyperbolic functions.
In this term's lectures, we study continuity of functions of a real or complex variable, and differentiability of functions of a real variable.
See the examinable syllabus.
Prof. Zhongmin Qian
At the end of the course students will be able to apply limiting properties to describe and prove continuity and differentiability conditions for real and complex functions. They will be able to prove important theorems, such as the Intermediate Value Theorem, Rolle's Theorem and Mean Value Theorem, and will continue the study of power series and their convergence.
Definition of the function limit. Definition of continuity of functions on subsets of \(\mathbb{R}\) and \(\mathbb{C}\) in terms of \(\varepsilon\) and \(\delta\). Continuity of real valued functions of several variables. The algebra of continuous functions; examples, including polynomials. Intermediate Value Theorem for continuous functions on intervals. Boundedness, maxima, minima and uniform continuity for continuous functions on closed intervals. Monotone functions on intervals and the Inverse Function Theorem.
Sequences and series of functions, uniform convergence. Weierstrass's M-test for uniformly convergent series of functions. Uniform limit of a sequence of continuous functions is continuous. Continuity of functions defined by power series.
Definition of the derivative of a function of a real variable. Algebra of derivatives, examples to include polynomials and inverse functions. The derivative of a function defined by a power series is given by the derived series (proof not examinable). Vanishing of the derivative at a local maximum or minimum. Rolle's Theorem, Mean Value Theorem, and Cauchy's (Generalized) Mean Value Theorem with applications: Constancy Theorem, monotone functions, exponential function and trigonometric functions. L'Hôpital's Formula. Taylor's Theorem with remainder in Lagrange's form; examples. The binomial expansion with arbitrary index.
In these lectures we define a simple integral and study its properties; prove the Mean Value Theorem for Integrals and the Fundamental Theorem of Calculus. This gives us the tools to justify term-by-term differentiation of power series and deduce the elementary properties of the trigonometric functions.
See the examinable syllabus.
Prof. Marc Lackenby
At the end of the course students will be familiar with the construction of an integral from fundamental principles, including important theorems. They will know when it is possible to integrate or differentiate term-by-term and be able to apply this to, for example, trigonometric series.
Step functions, their integral, basic properties. Minorants and majorants of bounded functions on bounded intervals. Definition of Riemann integral.
The application of uniform continuity to show that continuous functions are Riemann integrable on closed bounded intervals; bounded continuous functions are Riemann integrable on bounded intervals.
Elementary properties of Riemann integrals: positivity, linearity, subdivision of the interval. The Mean Value Theorem for Integrals. The Fundamental theorem of Calculus; integration by parts and by substitution.
The interchange of integral and limit for a uniform limit of continuous functions on a bounded interval. Term-by-term integration and differentiation of a (real) power series (interchanging limit and derivative for a series of functions where the derivatives converge uniformly).
The course surveys the basics of stochastic calculus as a preparation for developments in the Michaelmas term courses
Prerequisites
It will be assumed that students have a good understanding of probability and measure and at least a first course in stochastic processes
1. Continuous martingales and Brownian motion
Basic theorems and properties of continuous martingales and continuous local martingales. Properties of Brownian motion.
2. Stochastic integration
Construction of the stochastic integral,L2theory and extension to local martingales and semi-martingales. Ito’s formula, all in the setting of continuous martingales.
3. Useful theorems
Dambanis-Dubins-Schwarz, Girsanov, Martingale Representation4: SDEs classical existence and uniqueness results
Strong and weak solutions. Pathwise uniqueness and uniqueness in law. Existence and uniqueness of strong solutions via picard
5. Martingale problems
Connections between SDEs and generators; weak solutions to SDEs through martingale problems
Prof. Ben Hambly
This is an 8 hour course held in the first two weeks of the CDT in Random Systems
Modelling and Analysis of Continuous Real-World Problems will introduce a number of key methods for studying continuum models. Each week we start from real-world problems and show how to derive the corresponding mathematical model. We then use these models as vehicles to demonstrate the relevant analytical and computational methods. At the end of each week, the students will have the complete set of tools needed to set up, analyse and solve a class of mathematical models.
Prof. Colin Please
Diffusion problems arising in heat flow, chemical reactions, pattern formation, and thermal runaway. Conservation laws; well-posedness; separation of variables; transforms; similarity solutions; nonlinear equilibria; linear stability.
Elastic waves; acoustics; Stokes waves; electromagnetism; optics. Method of characteristics; separation of variables; eigenvalue problems; resonance; high-frequency asymptotics.
River flow; porous-medium flow; two-phase flow. Shocks, causality, regularization, weakly nonlinear theory.
Capillary statics; elasticity; buckling; liquid crystals; Calculus of variations; bifurcations; weakly nonlinear analysis.
This course is based around a set of Matlab assignments, one per week. These cover a range of numerical analysis topics specializing in particular on the numerical solution of ODEs and PDEs. The lectures will go over the practical aspects required for each week's assignment, and discuss the results from the previous week's assignment when necessary. The lectures will focus on using Matlab to apply the Numerical Analysis theory learned in other classes.
Dr Kathryn Gillow
In these lectures we study the real and complex numbers, and study their properties, particularly completeness; define and study limits of sequences, convergence of series, and power series.
See the examinable syllabus.
Prof. Frances Kirwan
Students will have:
(i) an ability to work within an axiomatic framework;
(ii) a detailed understanding of how Cauchy's criterion for the convergence of real and complex sequences and series follows from the completeness axiom for \(\mathbb{R}\), and the ability to explain the steps in standard mathematical notation;
(iii) knowledge of some simple techniques for testing the convergence of sequences and series, and confidence in applying them;
(iv) familiarity with a variety of well-known sequences and series, with a developing intuition about the behaviour of new ones;
(v) an understanding of how the elementary functions can be defined by power series, with an ability to deduce some of their easier properties.
Real numbers: arithmetic, ordering, suprema, infima; the real numbers as a complete ordered field. Definition of a countable set. The countability of the rational numbers. The reals are uncountable. The complex number system. The triangle inequality.
Sequences of real or complex numbers. Definition of a limit of a sequence of numbers. Limits and inequalities. The algebra of limits. Order notation: \(O\), \(o\).
Subsequences; a proof that every subsequence of a convergent sequence converges to the same limit; bounded monotone sequences converge. Bolzano--Weierstrass Theorem. Cauchy's convergence criterion.
Series of real or complex numbers. Convergence of series. Simple examples to include geometric progressions and some power series. Absolute convergence, Comparison Test, Ratio Test, Integral Test. Alternating Series Test.
Power series, radius of convergence. Examples to include definition of and relationships between exponential, trigonometric functions and hyperbolic functions.
In this term's lectures, we study continuity of functions of a real or complex variable, and differentiability of functions of a real variable.
See the examinable syllabus.
Prof. Zhongmin Qian
At the end of the course students will be able to apply limiting properties to describe and prove continuity and differentiability conditions for real and complex functions. They will be able to prove important theorems, such as the Intermediate Value Theorem, Rolle's Theorem and Mean Value Theorem, and will continue the study of power series and their convergence.
Definition of the function limit. Definition of continuity of functions on subsets of \(\mathbb{R}\) and \(\mathbb{C}\) in terms of \(\varepsilon\) and \(\delta\). Continuity of real valued functions of several variables. The algebra of continuous functions; examples, including polynomials. Intermediate Value Theorem for continuous functions on intervals. Boundedness, maxima, minima and uniform continuity for continuous functions on closed intervals. Monotone functions on intervals and the Inverse Function Theorem.
Sequences and series of functions, uniform convergence. Weierstrass's M-test for uniformly convergent series of functions. Uniform limit of a sequence of continuous functions is continuous. Continuity of functions defined by power series.
Definition of the derivative of a function of a real variable. Algebra of derivatives, examples to include polynomials and inverse functions. The derivative of a function defined by a power series is given by the derived series (proof not examinable). Vanishing of the derivative at a local maximum or minimum. Rolle's Theorem, Mean Value Theorem, and Cauchy's (Generalized) Mean Value Theorem with applications: Constancy Theorem, monotone functions, exponential function and trigonometric functions. L'Hôpital's Formula. Taylor's Theorem with remainder in Lagrange's form; examples. The binomial expansion with arbitrary index.
In these lectures we define a simple integral and study its properties; prove the Mean Value Theorem for Integrals and the Fundamental Theorem of Calculus. This gives us the tools to justify term-by-term differentiation of power series and deduce the elementary properties of the trigonometric functions.
See the examinable syllabus.
Prof. Ben Green
At the end of the course students will be familiar with the construction of an integral from fundamental principles, including important theorems. They will know when it is possible to integrate or differentiate term-by-term and be able to apply this to, for example, trigonometric series.
Step functions, their integral, basic properties. Minorants and majorants of bounded functions on bounded intervals. Definition of Riemann integral.
The application of uniform continuity to show that continuous functions are Riemann integrable on closed bounded intervals; bounded continuous functions are Riemann integrable on bounded intervals.
Elementary properties of Riemann integrals: positivity, linearity, subdivision of the interval. The Mean Value Theorem for Integrals. The Fundamental theorem of Calculus; integration by parts and by substitution.
The interchange of integral and limit for a uniform limit of continuous functions on a bounded interval. Term-by-term integration and differentiation of a (real) power series (interchanging limit and derivative for a series of functions where the derivatives converge uniformly).
The course introduces the concept of likelihood for a probabilistic model and its use in estimating unknown model parameters. Models covered will include linear regression with one or two regressors. In many examples confidence intervals may be found by using the Central Limit Theorem (statement only). Model checking and outlier detection are core concepts that are broadly relevant across many aspects of mathematical modelling and will be explored here in the context of regression with one or two regressors. Regression models are an example of supervised learning, however a large part of statistics and data analysis can be classified as unsupervised learning, i.e. finding structure in data sets, e.g. data from financial markets, medical imaging, retail, population genetics, social networks. Techniques for finding structure in datasets are relevant to many parts of applied maths, specifically this course will cover principal components analysis and clustering techniques.
See the examinable syllabus.
Prof. Christl Donnelly
Dr Dino Sejdinovic
Students should have an understanding of likelihood, the use of maximum likelihood to find estimators, and some properties of the resulting estimators. They should have an understanding of confidence intervals and their construction using the Central Limit Theorem. They should have an understanding of linear regression with one or two regressors, and of finding structure in data sets using principal components and some clustering techniques.
Random samples, concept of a statistic and its distribution, sample mean as a measure of location and sample variance as a measure of spread.
Concept of likelihood; examples of likelihood for simple distributions. Estimation for a single unknown parameter by maximising likelihood. Examples drawn from: Bernoulli, binomial, geometric, Poisson, exponential (parametrized by mean), normal (mean only, variance known). Data to include simple surveys, opinion polls, archaeological studies, etc. Properties of estimators---unbiasedness, Mean Squared Error = (bias\(^{2}\) + variance). Statement of Central Limit Theorem (excluding proof). Confidence intervals using CLT. Simple straight line fit, \(Y_{t}=a+bx_{t}+\varepsilon_{t}\), with \(\varepsilon _{t}\) normal independent errors of zero mean and common known variance. Estimators for \(a\), \(b\) by maximising likelihood using partial differentiation, unbiasedness and calculation of variance as linear sums of \(Y_{t}\). (No confidence intervals). Examples (use scatter plots to show suitability of linear regression).
Linear regression with 2 regressors. Special case of quadratic regression \(Y_t = a + bx_t + cx^2_t + \epsilon_t\). Model diagnostics and outlier detection. Residual plots. Heteroscedasticity.
Outliers and studentized residuals. High-leverage points and leverage statistics. [2.5]
Introduction to unsupervised learning with real world examples. Principal components analysis (PCA). Proof that PCs maximize directions of maximum variance and are orthogonal using Lagrange multipliers. PCA as eigendecomposition of covariance matrix. Eigenvalues as variances. Choosing number of PCs. The multivariate normal distribution pdf. Examples of PCA on multivariate normal data and clustered data. [3]
Clustering techniques; K-means clustering. Minimization of within-cluster variance. K-means algorithm and proof that it will decrease objective function. Local versus global optima and use of random initializations. Hierarchical clustering techniques. Agglomerative clustering using complete, average and single linkage [2.5]
The theory of functions of a complex variable is a rewarding branch of mathematics to study at the undergraduate level with a good balance between general theory and examples. It occupies a central position in mathematics with links to analysis, algebra, number theory, potential theory, geometry, topology, and generates a number of powerful techniques (for example, evaluation of integrals) with applications in many aspects of both pure and applied mathematics, and other disciplines, particularly the physical sciences.
In these lectures we begin by introducing students to the language of topology before using it in the exposition of the theory of (holomorphic) functions of a complex variable.
The central aim of the lectures is to present Cauchy's Theorem and its consequences, particularly series expansions of holomorphic functions, the calculus of residues and its applications.
The course concludes with an account of the conformal properties of holomorphic functions and applications to mapping regions.
Prof. Kevin McGerty
Students will have been introduced to point-set topology and will know the central importance of complex variables in analysis. They will have grasped a deeper understanding of differentiation and integration in this setting and will know the tools and results of complex analysis including Cauchy's Theorem, Cauchy's integral formula, Liouville's Theorem, Laurent's expansion and the theory of residues.
Metric Spaces (10 lectures)
Basic definitions: metric spaces, isometries, continuous functions (\(\varepsilon-\delta\) definition), homeomorphisms, open sets, closed sets. Examples of metric spaces, including metrics derived from a norm on a real vector space, particularly \(l^1, l^2, l^\infty\) norms on \(\mathbb{R}^n\), the sup norm on the bounded real-valued functions on a set, and on the bounded continuous real-valued functions on a metric space. The characterisation of continuity in terms of the pre-image of open sets or closed sets. The limit of a sequence of points in a metric space. A subset of a metric space inherits a metric. Discussion of open and closed sets in subspaces. The closure of a subset of a metric space. [3]
Completeness (but not completion). Completeness of the space of bounded real-valued functions on a set, equipped with the norm, and the completeness of the space of bounded continuous real-valued functions on a metric space, equipped with the metric. Lipschitz maps and contractions. Contraction Mapping Theorem. [2.5]
Connected metric spaces, path-connectedness. Closure of a connected space is connected, union of connected sets is connected if there is a non-empty intersection, continuous image of a connected space is connected. Path-connectedness implies connectedness. Connected open subset of a normed vector space is path-connected. [2]
Definition of sequential compactness and proof of basic properties of compact sets. Preservation of compactness under continuous maps, equivalence of continuity and uniform continuity for functions on a compact set. Equivalence of sequential compactness with being complete and totally bounded. The Arzela-Ascoli theorem (proof non-examinable). Open cover definition of compactness. Heine-Borel (for the interval only) and proof that compactness implies sequential compactness (statement of the converse only). [2.5]
Complex Analysis (22 lectures)
Basic geometry and topology of the complex plane, including the equations of lines and circles. Extended complex plane, Riemann sphere, stereographic projection. Möbius transformations acting on the extended complex plane. Möbius transformations take circlines to circlines. [3]
Complex differentiation. Holomorphic functions. Cauchy-Riemann equations (including \(z,\bar{z}\) version). Real and imaginary parts of a holomorphic function are harmonic. [2]
Recap on power series and differentiation of power series. Exponential function and logarithm function. Fractional powers — examples of multifunctions. The use of cuts as method of defining a branch of a multifunction. [3]
Path integration. Cauchy's Theorem. (Sketch of proof only — students referred to various texts for proof.) Fundamental Theorem of Calculus in the path integral/holomorphic situation. [2]
Cauchy's Integral formulae. Taylor expansion. Liouville's Theorem. Identity Theorem. Morera's Theorem. [4]
Laurent's expansion. Classification of isolated singularities. Calculation of principal parts, particularly residues. [2]
Residue Theorem. Evaluation of integrals by the method of residues (straightforward examples only but to include the use of Jordan's Lemma and simple poles on contour of integration). [3]
Conformal mappings. Riemann mapping theorem (no proof): Möbius transformations, exponential functions, fractional powers; mapping regions (not Christoffel transformations or Joukowski's transformation). [3]
Scientific computing pervades our lives: modern buildings and structures are designed using it, medical images are reconstructed for doctors using it, the cars and planes we travel on are designed with it, the pricing of "Instruments'' in the financial market is done using it, tomorrow's weather is predicted with it. The derivation and study of the core, underpinning algorithms for this vast range of applications defines the subject of Numerical Analysis. This course gives an introduction to that subject.
Through studying the material of this course students should gain an understanding of numerical methods, their derivation, analysis and applicability. They should be able to solve certain mathematically posed problems using numerical algorithms. This course is designed to introduce numerical methods - i.e. techniques which lead to the (approximate) solution of mathematical problems which are usually implemented on computers. The course covers derivation of useful methods and analysis of their accuracy and applicability.
The course begins with a study of methods and errors associated with computation of functions which are described by data values (interpolation or data fitting). Following this we turn to numerical methods of linear algebra, which form the basis of a large part of computational mathematics, science, and engineering. Key ideas here include algorithms for linear equations, least squares, and eigenvalues built on LU and QR matrix factorizations. The course will also include the simple and computationally convenient approximation of curves: this includes the use of splines to provide a smooth representation of complicated curves, such as arise in computer aided design. Use of such representations leads to approximate methods of integration. Techniques for improving accuracy through extrapolation will also be described. The course requires elementary knowledge of functions and calculus and of linear algebra.
Although there are no assessed practicals for this course, the tutorial work involves a mix of written work and computational experiments. Knowledge of Matlab is required, but many examples will be provided.
Prof. Yuji Nakatsukasa
At the end of the course the student will know how to:
Find the solution of linear systems of equations.
Compute eigenvalues and eigenvectors of matrices.
Approximate functions of one variable by polynomials and piecewise polynomials (splines).
Compute good approximations to one-dimensional integrals.
Increase the accuracy of numerical approximations by extrapolation.
Use computing to achieve these goals.
Lagrange interpolation [1 lecture]
Newton-Cotes quadrature [2 lectures]
Gaussian elimination and LU factorization [2 lectures]
QR factorization [1 lecture]
Eigenvalues: Gershgorin's Theorem, symmetric QR algorithm, polynomial rootfinding via eigenvalues [3 lectures]
Best approximation in inner product spaces, least squares, orthogonal polynomials [4 lectures]
Piecewise polynomials, splines [2 lectures]
Richardson Extrapolation. [1 lecture].
Part A Integration is essential; the only concepts which will be used are the convergence theorems and the theorems of Fubini and Tonelli, and the notions of measurable functions, integrable functions, null sets and L^p spaces. No knowledge is needed of outer measure, or of any particular construction of the integral, or of any proofs. A good working knowledge of Part A Core Analysis (both metric spaces and complex analysis) is expected.
The course provides an introduction to the methods of functional analysis.
It builds on core material in analysis and linear algebra studied in Part A. The focus is on normed spaces and Banach spaces; a brief introduction to Hilbert spaces is included, but a systematic study of such spaces and their special features is deferred to B4.2 Functional Analysis II. The techniques and examples studied in the Part B courses Functional Analysis I and II support, in a variety of ways, many advanced courses, in particular in analysis and partial differential equations, as well as having applications in mathematical physics and other areas.
Prof. Melanie Rupflin
Students will have a firm knowledge of real and complex normed vector spaces, with their geometric and topological properties. They will be familiar with the notions of completeness, separability and density, will know the properties of a Banach space and important examples, and will be able to prove results relating to the Hahn-Banach Theorem. They will have developed an understanding of the theory of bounded linear operators on a Banach space.
Brief recall of material from Part A Metric Spaces and Part A Linear Algebra on real and complex normed vector spaces, their geometry and topology and simple examples of completeness. The norm associated with an inner product and its properties. Banach spaces, exemplified by \(\ell^p\), \(L^p\), \(C(K)\), spaces of differentiable functions. Finite-dimensional normed spaces, including equivalence of norms and completeness. Hilbert spaces as a class of Banach spaces having special properties (illustrations, but no proofs); examples (Euclidean spaces, \(\ell^2\), \(L^2\)).
Density. Approximation of functions, Stone-Weierstrass Theorem. Separable spaces; separability of subspaces.
Bounded linear operators, examples (including integral operators). Continuous linear functionals. Dual spaces. Hahn-Banach Theorem (proof for separable spaces only); applications, including density of subspaces and embedding of a normed space into its second dual. Adjoint operators.
Spectrum and resolvent. Spectral mapping theorem for polynomials.
Part A Integration is essential. A good working knowledge of Part A core Analysis is expected. Part A Integral Transforms and Introduction to Manifolds are desirable but not essential.
Distribution theory can be thought of as the completion of differential calculus, just as Lebesgue integration theory can be thought of as the completion of integral calculus. It was created by Laurent Schwartz in the 20th century, as was Lebesgue's integration theory.
In this course we give an introduction to distributions. It builds on core material in analysis and integration studied in Part A. One of the main areas of applications of distributions is to the theory of partial differential equations, and a brief treatment, mainly through examples, is included. A more systematic study is deferred to Fourier Analysis and PDEs.
Prof. Jan Kristensen
Students will become acquainted with the basic techniques that in many situations form the starting point for the modern treatment of PDEs.
Test functions and distributions on \(\mathbb{R}^n\): definitions and examples, Dirac \(\delta \)- function, approximate identities and constructions using convolution of functions. Density of test functions in Lebesgue spaces. Smooth partitions of unity. [4 lectures]
The calculus of distributions on \(\mathbb{R}^n\) : functions as distributions, operations on distributions, adjoint identities, consistency of derivatives, convolution of test functions and distributions. The Fundamental Theorem of Calculus for distributions. Support and singular support of a distribution. [6 lectures]
Examples of distributions defined by principal value integrals and finite parts. Examples of distributional boundary values of holomorphic functions defined in a half plane. [2 lectures]
Distributional and weak solutions of PDEs, absolutely continuous functions, Sobolev functions. Examples of fundamental solutions. Weyl's Lemma for distributions. [4 lectures]
B4.1 Functional Analysis I is an essential pre-requisite. A4 Integration is also essential; the only concepts which will be used are the convergence theorems and the theorems of Fubini and Tonelli, and the notions of measurable functions, integrable functions, null sets and L^p spaces. No knowledge is needed of outer measure, or of any particular construction of the integral, or of any proofs. A good working knowledge of Part A Core Analysis (both metric spaces and complex analysis) is expected.
The course provides further introduction to the methods of functional analysis. It builds on core material in Part A analysis and linear algebra and in Part B B4.1 Functional Analysis I. On one hand, it delves deeper into operator theory on Banach spaces, and on the other, it gives a systematic study of Hilbert spaces, operators on Hilbert spaces and their special features. The techniques and examples studied in the course, together with that in B4.1, support, in a variety of ways, many advanced courses, in particular in analysis and partial differential equations, as well as having applications in mathematical physics and other areas.''
Prof. Luc Nguyen
Students will appreciate the role of completeness through the Baire category theorem and its consequences for operators on Banach spaces. They will have a demonstrable knowledge of the properties of a Hilbert space, including orthogonal complements, orthonormal sets, complete orthonormal sets together with related identities and inequalities. They will be familiar with the theory of linear operators on a Hilbert space, including adjoint operators, self-adjoint and unitary operators with their spectra. They will know the \(L^2\)-theory of Fourier series and be aware of the classical theory of Fourier series and other orthogonal expansions.
Hilbert spaces; examples including \(L^2\)-spaces. Orthogonality, orthogonal complement, closed subspaces, projection theorem. Riesz Representation Theorem.
Linear operators on Hilbert space, adjoint operators. Self-adjoint operators, orthogonal projections, unitary operators.
Baire Category Theorem and its consequences for operators on Banach spaces (Uniform Boundedness, Open Mapping, Inverse Mapping and Closed Graph Theorems). Strong convergence of sequences of operators.
Weak convergence. Weak precompactness of the unit ball.
Spectral theory in Hilbert spaces, in particular spectra of self-adjoint and unitary operators.
Orthonormal sets, Pythagoras, Bessel's inequality. Complete orthonormal sets, Parseval. \(L^2\)-theory of Fourier series, including completeness of the trigonometric system. Examples of other orthogonal expansions (Legendre, Laguerre, Hermite etc.).
Brief contextual comments on the classical theory of Fourier series and modes of convergence; exposition of failure of pointwise convergence of Fourier series of some continuous functions.
Distribution Theory and Analysis of PDEs is a pre-requisite.
Distribution theory can be thought of as the completion of differential calculus, just as Lebesgue integration theory can be thought of as the completion of integral calculus. It was created by Laurent Schwartz in the 20th century, as was Lebesgue's integration theory.
Distribution theory is a powerful tool that works very well in conjunction with the theory of Fourier transforms. One of the main areas of applications is to the theory of partial differential equations. In this course we give an introduction to these three theories.
Prof. Jan Kristensen
Students will become acquainted with the basic techniques that in many situations form the starting point for the modern treatment of PDEs.
The Fourier transform on \(\mathbb{R}^n\): the Schwartz class \(\mathcal{S}\) of test functions on \(\mathbb{R}^n\) , properties of the Fourier transform on \(\mathcal{S}\), the Fourier transform of a Gaussian and the inversion formula on \(\mathcal{S}\). [4 lectures]
The class of tempered distributions \(\mathcal{S}'\) and their calculus. Fourier transforms of tempered distributions: definitions and examples, convolutions with tempered distributions. The inversion formula on \(\mathcal{S}'\). Fourier transfor in \(L^2\) and ~Plancherel's theorem. [5 lectures]
Solving PDEs using Fourier transformation: the Laplace equation, the heat equation, the wave equation, Schrödinger's equation. Fundamental solutions, ellipticity and hypoellipticity. [3 lectures]
Fourier Analysis: the Riemann-Lebesgue lemma, Paley-Wiener theorems, the Poisson summation formula, the uncertainty principle.[4 lectures]
Students wishing to take this course are expected to have a thorough understanding of the basic theory of normed vector spaces (including properties and standard examples of Banach and Hilbert spaces, dual spaces, and the Hahn-Banach theorem) and of bounded linear operators (ideally including the Open Mapping Theorem, the Inverse Mapping Theorem and the Closed Graph Theorem). Some fluency with topological notions such as (sequential) compactness and bases of topological spaces will also be assumed, as will be basic familiarity with the Lebesgue integral. A number of these prerequisites will be reviewed (briefly) during the course, and there will be a document available on the course webpage summarising most of the relevant background material.
This course builds on what is covered in introductory courses on Functional Analysis, by extending the theory of Banach spaces and operators. As well as developing general methods that are useful in operator theory, we shall look in more detail at the structure and special properties of "classical'' sequence spaces and function spaces.
Prof. Stuart White
Normed vector spaces and Banach spaces. Dual spaces. Direct sums and complemented subspaces. Quotient spaces and quotient operators.
The Baire Category Theorem and its consequences (review).
Hahn-Banach extension and separation theorems. The bidual space. Reflexivity. Completion of a normed vector space.
Convexity and smoothness of norms. Lebesgue spaces and their duals.
Weak and weak* topologies. The Banach-Alaoglu theorem. Goldstine's theorem. Equivalence of reflexivity and weak compactness of the closed unit ball. The Schur property of \(\ell^1\). Weakly compact operators.
Compactness in normed vector spaces. The Arzelà-Ascoli theorem. Compact operators. Schauder's theorem on compactness of dual operators. Completely continuous operators.
The Closed Range Theorem. Fredholm theory: Fredholm operators; the Fredholm index; perturbation results; the Fredholm Alternative. Spectral theory of compact operators. The Spectral Theorem for compact self-adjoint operators.
Schauder bases; examples in classical Banach spaces.
The only necessary prerequisite is a basic complex analysis course: analytic functions, Taylor series, contour integration, Cauchy theorems, residues, maximum modulus, Liouville's theorem. Some basic results that might not be covered by a basic course (such as argument principle and the Rouche theorem) will be given in the introductory chapter of the lecture notes.
The theory of conformal maps has a very long and rich history. Its foundations were laid out by Riemann in mid XIX century and it was one of the central areas of the Complex Analysis since then.
The aim of the course is to teach the principal techniques and methods of analytic and geometric function theory. We start by proving the Riemann mapping theorem which claims that all non-trivial simply connected domains are conformal images of the unit disc. This provides a connection between planar domains and 1-to-1, or univalent, analytic functions in the unit disc.
In this course we will study the Riemann mapping theorem and general theory of univalent functions, study how geometric properties of domains are related to analytic properties of functions. We will also study conformal invariants: quantities that do not change under conformal transformations. We will see that many of these invariants have a simple geometric interpretation and could be used to obtain numerous beautiful results.
Prof. Dmitry Belyaev
Students will have been introduced to ideas and techniques of geometric function theory that play important role and have a lot of applications in other areas of analysis. In particular, they will learn the proof of the Riemann mapping theorem and the concept of conformal invariants.
We will cover the following topics:
- The Riemann mapping theorem. The main goal will be to prove Riemann's theorem which tells us that any non-trivial simply-connected domain can be conformally mapped onto the unit disc. This will be the key result for the entire course since it will allow us to connect the geometry of a domain with the analytical properties of a conformal map which sends this domain to the unit disc. Within this section we will discuss
- Möbius transformations and the Schwarz lemma
- Normal families, the Montel and Hurwitz theorems
- Proof of Riemann mapping theorem
- Constructive uniformization: Christoffel-Schwarz mappings and iterative methods
- Boundary correspondence, accessible points and prime ends
- Uniformization of multiply-connected domains (without proof)
- Applications: the Dirichlet boundary problem
- Area theorem and coefficient estimates
- The Koebe \(1/4\) theorem, distortion theorems
- Conformal invariants: extremal length and its applications
- Conformal invariance of Green's function and harmonic measure
- Extremal length, its conformal invariance and applications
Integration and measure theory, martingales in discrete and continuous time, stochastic calculus. Functional analysis is useful but not essential.
Stochastic analysis and partial differential equations are intricately connected. This is exemplified by the celebrated deep connections between Brownian motion and the classical heat equation, but this is only a very special case of a general phenomenon. We explore some of these connections, illustrating the benefits to both analysis and probability.
Ilya Chevyrev
The student will have developed an understanding of the deep connections between concepts from probability theory, especially diffusion processes and their transition semigroups, and partial differential equations.
Feller processes and semigroups. Resolvents and generators. Hille-Yosida Theorem (without proof). Diffusions and elliptic operators, convergence and approximation. Stochastic differential equations and martingale problems. Duality. Speed and scale for one dimensional diffusions.
Green's functions as occupation densities. The Dirichlet and Poisson problems. Feynman-Kac formula.