Guide Elementary Mathematical and Computational Tools For Electrical and Computer Engineers Using MATLAB

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Exposure of student to practice of performing mathematical experiments on computer, with emphasis on probability. Mathematical treatment of random number generation and application of these tools to mathematical topics in Monte Carlo method, limit theorems and stochastic processes for purpose of gaining mathematical insight. Modern introduction to Probability Theory and Stochastic Processes.

The choice of material is motivated by applications to problems such as queueing networks, filtering and financial mathematics.

Manassah, Jamal T. [WorldCat Identities]

Topics include: review of discrete probability and continuous random variables, random walks, markov chains, martingales, stopping times, erodicity, conditional expectations, continuous-time Markov chains, laws of large numbers, central limit theorem and large deviations. Prerequisite: MA ST Stochastic models of financial markets. No-arbitrage derivativepricing.

From discrete to continuous time models. Brownian motion, stochastic calculus, Feynman-Kac formula and tools for European options and equivalent martingale measures. Black-Scholes formula. Hedging strategies and management of risk. Optimal stopping and American options. Term structure models and interest rate derivatives. Stochastic volatility models. Monte Carlo MC methods for accurate option pricing, hedging and risk management.

Modeling using stochastic asset models e. Stochastic models, including use of random number generators, random paths and discretization methods e. Euler-Maruyama method , and variance reduction. Implementation using Matlab. Incorporation of the latest developments regarding MC methods and their uses in Finance.

Mathematics (MA)

This course focuses on mathematical methods to analyze and manage risks associated with financial derivatives. Topics covered include aggregate loss distributions, extreme value theory, default probabilities, Value-at-Risk and expected shortfall, coherent risk measures, correlation and copula, applications of principle component analysis and Monte Carlo simulations in financial risk management, how to use stochastic differential equations to price financial risk derivatives, and how to back-test and stress-test models.

Set theory, topological spaces, metric spaces, continuous functions, separation, cardinality properties, product and quotient topologies, compactness, connectedness. An introduction to smooth manifolds. Topics include: topological and smooth manifolds, smooth maps and differentials, vector fields and flows, Lie derivatives, vector bundles, tensors, differential forms, exterior calculus, and integration on manifolds.

Logic and axiomatic approach, the Zermelo-Fraenkel axioms and other systems, algebra of sets and order relations, equivalents of the Axiom of Choice, one-to-one correspondences, cardinal and ordinal numbers, the Continuum Hypothesis. Introduction to model development for physical and biological applications. Mathematical and statistical aspects of parameter estimation.

Compartmental analysis and conservation laws, heat transfer, and population and disease models. Analytic and numerical solution techniques and experimental validation of models. Knowledge of high-level programming languages required. Model development, using Newtonian and Hamiltonian principles, for acoustic and fluid applications, and structural systems including membranes, rods, beams, and shells.

Fundamental aspects of electromagnetic theory. Introduction to basic parallel architectures, algorithms and programming paradigms; message passing collectives and communicators; parallel matrix products, domain decomposition with direct and iterative methods for linear systems; analysis of efficiency, complexity and errors; applications such as 2D heat and mass transfer. Survey of finite difference methods for partial differential equations including elliptic, parabolic and hyperbolic PDE's. Consideration of both linear and nonlinear problems.

Theoretical foundations described; however, emphasis on algorithm design and implementation. Introduction to finite element method. Applications to both linear and nonlinear elliptic and parabolic partial differential equations. Investigation of some topic in mathematics to a deeper and broader extent than typically done in a classroom situation.

For the applied mathematics student the topic usually consists of a realistic application of mathematics to student's minor area. A written and oral report on the project required. Prerequisite: Master's student. Teaching experience under the mentorship of faculty who assist the student in planning for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

For students in non-thesis master's programs who have completed all credit hour requirements for their degree but need to maintain full-time continuous registration to complete incomplete grades, projects, final master's exam, etc. Students may register for this course a maximum of one semester.

For students in non thesis master's programs who have completed all other requirements of the degree except preparing for and taking the final master's exam. Instruction in research and research under the mentorship of a member of the Graduate Faculty. For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research.

For students who have completed all credit hour requirements and full-time enrollment for the master's degree and are writing and defending their thesis. Credits Arranged. General integer programming problems and principal methods of solving them. Emphasis on intuitive presentation of ideas underlying various algorithms rather than detailed description of computer codes. Students have some "hands on" computing experience that should enable them to adapt ideas presented in course to integer programming problems they may encounter.

Integration: Legesgue measure and integration, Lebesgue-dominated convergence and monotone convergence theorems, Fubini's theorem, extension of the fundamental theorem of calculus. Banach spaces: Lp spaces, weak convergence, adjoint operators, compact linear operators, Fredholm-Fiesz Schauder theory and spectral theorem. Advanced topics in functional analysis such as linear topological spaces; Banach algebra, spectral theory and abstract measure theory and integration.

Definition of Lie algebras and examples. Nilpotent, solvable and semisimple Lie algebras. Engel's theorem, Lie's Theorem, Killing form and Cartan's criterion. Weyl's theorem on complete reducibility. Representations of s1 2,C. Root space decomposition of semisimple Lie algebras. Root system and Weyl group. Field extensions, Galois theory, modules, tensor products, exterior products. Effective algorithms for symbolic matrices, commutative algebra, real and complex algebraic geometry, and differential and difference equations.

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The emphasis is on the algorithmic aspects. Canonical forms, functions of matrices, variational methods, perturbation theory, numerical methods, nonnegative matrices, applications to differential equations, Markov chains.

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Polytopes V-polytopes and H-polytopes. Fourier-Motzkin elimination, Farkas Lemma, face numbers of polytopes, graphs of polytopes, linear programming for geometers, Balinski's Theorem, Steinitz' Theorem, Schlegel diagrams, polyhedral complexes, shellability, and face rings. Semisimple Lie algebras, root systems, Weyl groups, Cartan matrices and Dynkin diagrams, universal enveloping algebras, Serre's Theorem, Kac-Moody algebras, highest weight representations of finite dimensional semisimple algebras and affine Lie algebras, Kac-Weyl character formula.

Prerequisite: MA Stability of equilibrium points for nonlinear systems. Liapunov functions. Unconstrained and constrained optimal control problems. Pontryagin's maximum principle and dynamic programming.

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Computation with gradient methods and Newton methods. Multidisciplinary applications. Existence-uniqueness theory, periodic solutions, invariant manifolds, bifurcations, Fredholm's alternative. Linear second order parabolic, elliptic and hyperbolic equations. Initial value problems and boundary value problems. Iterative and variational methods. Existence, uniqueness and regularity. Nonlinear equations and systems. Homotopy, fundamental group, covering spaces, classification of surfaces, homology and cohomology.

An introduction to smooth manifolds with metric. Topics include: Riemannian metric and generalizations, connections, covariant derivatives, parallel translation, Riemannian or Levi-Civita connection, geodesics and distance, curvature tensor, Bianchi identities, Ricci and scalar curvatures, isometric embeddings, Riemannian submanifolds, hypersurfaces, Gauss Bonnet Theorem; applications and connections to other fields. Prerequisite: Advanced calculus, reasonable background in biology. Role of theory construction and model building in development of experimental science. Historical development of mathematical theories and models for growth of one-species populations logistic and off-shoots , including considerations of age distributions matrix models, Leslie and Lopez; continuous theory, renewal equation.

Some of the more elementary theories on the growth of organisms von Bertalanffy and others; allometric theories; cultures grown in a chemostat. Mathematical theories oftwo and more species systems predator-prey, competition, symbosis; leading up to present-day research and discussion of some similar models for chemical kinetics. Much emphasis on scrutiny of biological concepts as well as of mathematical structureof models in order to uncover both weak and strong points of models discussed.