جامعة النجاح الوطنية
An-Najah National University
Computerized Mathematics
Duration: 24 Months (2 Years)
Degree Awarded: MSc
Student must complete 36 credit hours

Speciality Requirements Student must complete 18 credit hours

Course Code Course Name Credit Hours Prerequests
3
Topics covered in this course include initial–value problems, Taylor, Euler, Runge-Kutta Euler predictor – corrector combination, linear multistep methods (LMM), local truncation errors and boundary-value problems.
3
This course looks at finite difference approximations, hyperbolic equations, parabolic equations and elliptic equations.
3
Topics covered in this course include the spanning tree, route, maximum flow, assignment problem, transportation and transshipment problems, multi-stage problem solving, decomposition and recursive equations for final state and initial-final state optimisation.
3
This course looks at decision theory and games, inventory models, queuing theory and some optimisation techniques.
3
This course consists of a survey of current statistical software, numerical methods of statistical computations, non-linear optimisation, statistical simulation and recent in computer–intensive statistical methods.
3
This course consists of a review of vectors, matrices and linear equations, review of eigenvalues and eigenvectors, direct computational methods for solving linear equations , iterative computational methods for solving linear equations, Jacobi, Gauss-Seidel and SOR methods, convergence and divergence, computational methods for solving eigenvalue problems, power and inverse power methods, Sturm sequences, similarity transformations and LR and QR algorithms.
0
A graduate student must pass a qualifying examination (See Academic Rules and Regulations for Graduate Students).

Speciality Optional Requirements Student must complete 18 credit hours

Course Code Course Name Credit Hours Prerequests
3
Vector Spaces, Linear Transformations, linear Functionals, Dual Space, Characteristic Polynomials and Minimal Polynomials of Linear Transformations, Diagonalizations, Jordan and Rational Forms of Maticies, Cayley Hamilton Theorem, Inner Product Spaces.
3
This course consists of studying special mathematical topics approved by the department.
3
This course consists of studying special mathematical topics approved by the department.
3
Topics covered in this course include normed and inner product vector spaces, normed of bounded linear operator, convergence and completeness, Banach and Hilbert spaces, contraction mapping theorem weak, elements of lax theory, approximation of weakly formulated problems and application in integral equations.
3
This course consists of an introduction and vector analysis of simplex method, duality and sensitivity, special simplex forms, transportation and assignment problems and networks and linear programming.
3
This course looks at iterative methods for non-linear equations, systems of linear equations, system of nonlinear equations and conjugate gradient methods.
3
Polynomial Approximations - Chebyshev and least squares approximation, orthogonal polynomials, B-splines, Bezier curves, interpolation, linear methods of approximation, Trigonometric Polynomial Approximation - Fourier series and the FFT, orthogonal polynomials on the unit circle, Wavelet Approximation - Bernstein and Jackson Theorems, Strang-Fix condition, fast decomposition reconstruction, multiwavelet approximation, Error and Mesh Generation - Triangulation of surfaces, mesh and order selection, accuracy and regularity, Multivariate Problems, Applications : Graphics, signal processing, integration, and data compression
3
This course looks at positive definite matrices, their factorisations and minimum principle, least squares estimation, differential equations of equilibrium, complex integration and conformal mapping, Fourier series, the Fourier matrix, Fast Fourier transform and convolution.
3
This course consists of a survey of several of the main ideas of general graph theory with applications to network theory, oriented and non-oriented linear graphs, spanning trees, branches and connectivity, accessibility, planar graphs, networks and flows, matching and applications.
3
Topics covered in this course include fuzzy sets, fuzzy numbers, ranking of fuzzy numbers, fuzzy difference equations, fuzzy matrices, fuzzy vector spaces, decision – making with fuzzy preference relation, fuzzy relation equation and fuzzy logic.

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