PirateBay Download | stewart d numerical analysis graduate course 2022

stewart d numerical analysis graduate course 2022

Type:: E-books
Files:: 10
Size:: 401.5 MB
Uploaded On:: Dec. 8, 2022, 2:32 p.m.
Added By:: andryold1
Seeders:: 0
Leechers:: 0
Info Hash:: 8A2B1E1161490052F850874BFCD3D5CAD016A964

Textbook in PDF format This book aims to introduce graduate students to the many applications of numerical computation, explaining in detail both how and why the included methods work in practice. The text addresses numerical analysis as a middle ground between practice and theory, addressing both the abstract mathematical analysis and applied computation and programming models instrumental to the field. While the text uses pseudocode, Matlab and Julia codes are available online for students to use, and to demonstrate implementation techniques. The textbook also emphasizes multivariate problems alongside single-variable problems and deals with topics in randomness, including stochastic differential equations and randomized algorithms, and topics in optimization and approximation relevant to machine learning. Ultimately, it seeks to clarify issues in numerical analysis in the context of applications, and presenting accessible methods to students in mathematics and data science. Basics of Numerical Computation How Computers Work The Central Processing Unit Code and Data On Being Correct On Being Efficient Recursive Algorithms and Induction Working in Groups: Parallel Computing BLAS and LAPACK Exercises Programming Languages MATLABTM Julia Python C/C++ and Java Fortran Exercises Floating Point Arithmetic The IEEE Standards Correctly Rounded Arithmetic Future of Floating Point Arithmetic Exercises When Things Go Wrong Underflow and Overflow Subtracting Nearly Equal Quantities Numerical Instability Adding Many Numbers Exercises Measuring: Norms What Is a Norm? Norms of Functions Exercises Taylor Series and Taylor Polynomials Taylor Series in One Variable Taylor Series and Polynomials in More than One Variable Vector-Valued Functions Exercises Project Computing with Matrices and Vectors Solving Linear Systems Gaussian Elimination LU Factorization Errors in Solving Linear Systems Pivoting and PA=LU Variants of LU Factorization Exercises Least Squares Problems The Normal Equations QR Factorization Exercises Sparse Matrices Tridiagonal Matrices Data Structures for Sparse Matrices Graph Models of Sparse Factorization Unsymmetric Factorizations Exercises Iterations Classical Iterations Conjugate Gradients and Krylov Subspaces Non-symmetric Krylov Subspace Methods Exercises Eigenvalues and Eigenvectors The Power Method & Google Schur Decomposition The QR Algorithm Singular Value Decomposition The Lanczos and Arnoldi Methods Exercises Solving nonlinear equations Bisection method Convergence Robustness and reliability Exercises Fixed-point iteration Convergence Robustness and reliability Multivariate fixed-point iterations Exercises Newton's method Convergence of Newton's method Reliability of Newton's method Variant: Guarded Newton method Variant: Multivariate Newton method Exercises Secant and hybrid methods Convenience: Secant method Regula Falsi Hybrid methods: Dekker's and Brent's methods Exercises Continuation methods Following paths Numerical methods to follow paths Exercises Project Approximations and Interpolation Interpolation—Polynomials Polynomial Interpolation in One Variable Lebesgue Numbers and Reliability Exercises Interpolation—Splines Cubic Splines Higher Order Splines in One Variable Exercises Interpolation—Triangles and Triangulations Interpolation over Triangles Interpolation over Triangulations En FigdPrint eps Approximation Error over Triangulations Creating Triangulations Exercises Interpolation—Radial Basis Functions Exercises Approximating Functions by Polynomials Weierstrass' Theorem Jackson's Theorem Approximating Functions on Rectangles and Cubes Seeking the Best—Minimax Approximation Chebyshev's Equi-oscillation Theorem Chebyshev Polynomials and Interpolation Remez Algorithm Minimax Approximation in Higher Dimensions Exercises Seeking the Best—Least Squares Solving Least Squares Orthogonal Polynomials Trigonometric Polynomials and Fourier Series Chebyshev Expansions Exercises Project Integration and Differentiation Integration via Interpolation Rectangle, Trapezoidal and Simpson's Rules Newton–Cotes Methods Product Integration Methods Extrapolation Gaussian Quadrature Orthogonal Polynomials Reprise Orthogonal Polynomials and Integration Why the Weights are Positive Multidimensional Integration Tensor Product Methods Lagrange Integration Methods Symmetries and Integration Triangles and Tetrahedra High-Dimensional Integration Monte Carlo Integration Quasi-Monte Carlo Methods Numerical Differentiation Discrete Derivative Approximations Automatic Differentiation Differential Equations Ordinary Differential Equations — Initial Value Problems Basic Theory Euler's Method and Its Analysis Improving on Euler: Trapezoidal, Midpoint, and Heun Runge–Kutta Methods Multistep Methods Stability and Implicit Methods Practical Aspects of Implicit Methods Error Estimates and Adaptive Methods Differential Algebraic Equations (DAEs) Exercises Ordinary Differential Equations—Boundary Value Problems Shooting Methods Multiple Shooting Finite Difference Approximations Exercises Partial Differential Equations—Elliptic Problems Finite Difference Approximations Galerkin Method Handling Boundary Conditions Convection—Going with the Flow Higher Order Problems Exercises Partial Differential Equations—Diffusion and Waves Method of Lines Exercises Projects Randomness Probabilities and Expectations Random Events and Random Variables Expectation and Variance Averages Exercises Pseudo-Random Number Generators The Arithmetical Generation of Random Digits Modern Pseudo-Random Number Generators Generating Samples from Other Distributions Parallel Generators Exercises Statistics Averages and Variances Regression and Curve Fitting Hypothesis Testing Exercises Random Algorithms Random Choices Monte Carlo Algorithms and Markov Chains Exercises Stochastic Differential Equations Wiener Processes Itô Stochastic Differential Equations Stratonovich Integrals and Differential Equations Euler–Maruyama Method Higher Order Methods for Stochastic Differential Equations Exercises Project Optimization Basics of Optimization Existence of Minimizers Necessary Conditions for Local Minimizers Lagrange Multipliers and Equality-Constrained Optimization Exercises Convex and Non-convex Convex Functions Convex Sets Exercises Gradient Descent and Variants Gradient Descent Line Searches Convergence Stochastic Gradient Method Simulated Annealing Exercises Second Derivatives and Newton's Method Exercises Conjugate Gradient and Quasi-Newton Methods Conjugate Gradients for Optimization Variants on the Conjugate Gradient Method Quasi-Newton Methods Exercises Constrained Optimization Equality Constrained Optimization Inequality Constrained Optimization Exercises Project Appendix A What You Need from Analysis Banach and Hilbert Spaces Normed Spaces and Completeness Inner Products Dual Spaces and Weak Convergence Distributions and Fourier Transforms Distributions and Measures Fourier Transforms Sobolev Spaces Appendix References

Get This Torrent

Readme-!!!_Math.txt 857 bytes
Stewart D. Numerical Analysis. A Graduate Course 2022.pdf 7.6 MB
Sauer T. Numerical Analysis 2ed 2011.pdf 9.3 MB
Burden R., Faires J. Numerical Analysis 9ed 2011.pdf 14.5 MB
Sauer T. Numerical Analysis 3ed 2017.pdf 16.1 MB
Gerald C., Wheatley P. Applied Numerical Analysis 7ed 2004.pdf 29.6 MB
Atkinson K., Han W. Elementary Numerical Analysis 3ed 2004.pdf 36.3 MB
Atkinson K. Elementary numerical analysis 2ed 1993.pdf 52.6 MB
Burden R.,Faires J. Numerical Methods 3ed 2003.pdf 78.9 MB
Burden R., Faires J. Numerical Analysis 10ed 2016.pdf 156.7 MB

Details for: Stewart D. Numerical Analysis. A Graduate Course 2022

stewart d numerical analysis graduate course 2022

Similar Posts: