Numerical analysis | Numerical integration (quadrature)

Local linearization method

In numerical analysis, the local linearization (LL) method is a general strategy for designing numerical integrators for differential equations based on a local (piecewise) linearization of the given equation on consecutive time intervals. The numerical integrators are then iteratively defined as the solution of the resulting piecewise linear equation at the end of each consecutive interval. The LL method has been developed for a variety of equations such as the ordinary, delayed, random and stochastic differential equations. The LL integrators are key component in the implementation of inference methods for the estimation of unknown parameters and unobserved variables of differential equations given time series of (potentially noisy) observations. The LL schemes are ideals to deal with complex models in a variety of fields as neuroscience, finance, forestry management, control engineering, mathematical statistics, etc. (Wikipedia).

Local linearization method
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Local linearization

A "local linearization" is the generalization of tangent plane functions; one that can apply to multivariable functions with any number of inputs.

From playlist Multivariable calculus

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Linear Algebra for Computer Scientists. 7. Linear Combinations of Vectors

This computer science video is one of a series on linear algebra for computer scientists. In this video you will learn about linear combinations of vectors, that is, you will learn how to create new vectors by scaling then adding other vectors together. You will also learn that some sets

From playlist Linear Algebra for Computer Scientists

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Linearising nonlinear derivatives

A simple trick to linearise derivatives

From playlist Linearisation

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Determining if a vector is a linear combination of other vectors

Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Determining if a vector is a linear combination of other vectors

From playlist Linear Algebra

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Matrix of a matrix

Calculating the matrix of a linear transformation with respect to a basis B. Here is the case where the input basis is the same as the output basis. Check out my Vector Space playlist: https://www.youtube.com/watch?v=mU7DHh6KNzI&list=PLJb1qAQIrmmClZt_Jr192Dc_5I2J3vtYB Subscribe to my ch

From playlist Linear Transformations

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Linear Transformations and Linear Systems

In this video we discuss linear transformations. We start by examining the mathematical definition of a linear transformation and apply it to several examples including matrix multiplication and differentiation. We then see how linear transformations relate to linear systems (AKA linear

From playlist Linear Algebra

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Finding The Linearization of a Function Using Tangent Line Approximations

This calculus video tutorial explains how to find the local linearization of a function using tangent line approximations. It explains how to estimate function values by writing the equation of the tangent line and evaluating it. This video contains plenty of examples and practice proble

From playlist New Calculus Video Playlist

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Linear Algebra for Computer Scientists. 9. Decomposing Vectors

This computer science video is one of a series on linear algebra for computer scientists. In this video you will learn how to express a given vector as a linear combination of a set of given basis vectors. In other words, you will learn how to determine the coefficients that were used to

From playlist Linear Algebra for Computer Scientists

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Lecture 24 (CEM) -- Introduction to Variational Methods

This lecture introduces to the student to variational methods including finite element method, method of moments, boundary element method, and spectral domain method. It describes the Galerkin method for transforming a linear equation into matrix form as well as populating the global matr

From playlist UT El Paso: CEM Lectures | CosmoLearning.org Electrical Engineering

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Optimisation - an introduction: Professor Coralia Cartis, University of Oxford

Coralia Cartis (BSc Mathematics, Babesh-Bolyai University, Romania; PhD Mathematics, University of Cambridge (2005)) has joined the Mathematical Institute at Oxford and Balliol College in 2013 as Associate Professor in Numerical Optimization. Previously, she worked as a research scientist

From playlist Data science classes

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7. Solutions of Nonlinear Equations; Newton-Raphson Method

MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015 View the complete course: http://ocw.mit.edu/10-34F15 Instructor: James Swan This lecture talked about the system of non-linear equations. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/term

From playlist MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015

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Bala Krishnamoorthy (10/20/20): Dimension reduction: An overview

Bala Krishnamoorthy (10/20/20): Dimension reduction: An overview Title: Dimension reduction: An overview Abstract: We present a broad overview of various dimension reduction techniques. Referred to also as manifold learning, we review linear dimension reduction techniques, e.g., principa

From playlist Tutorials

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A Stationary Phase Method for a Class of Nonlinear Equations - Yen Do

Yen Do Georgia Institute of Technology October 26, 2010 In this talk I will describe a real-variable method to extract long-time asymptotics for solutions of many nonlinear equations (including the Schrodinger and mKdV equations). The method has many resemblances to the classical stationa

From playlist Mathematics

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Jianfeng Lu - Mathematical Models of Electronic Structure - IPAM at UCLA

Recorded 08 March 2022. Jianfeng Lu of Duke University presents "Mathematical Models of Electronic Structure" at IPAM's Advancing Quantum Mechanics with Mathematics and Statistics Tutorials. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/advancing-quantum-mechanics-with-

From playlist Tutorials: Advancing Quantum Mechanics with Mathematics and Statistics - March 8-11, 2022

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Marta D'Elia: A coupling strategy for nonlocal and local models with applications ...

The use of nonlocal models in science and engineering applications has been steadily increasing over the past decade. The ability of nonlocal theories to accurately capture effects that are difficult or impossible to represent by local Partial Differential Equation (PDE) models motivates a

From playlist HIM Lectures: Trimester Program "Multiscale Problems"

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Levon Nurbekyan: "Computational methods for nonlocal mean field games with applications"

High Dimensional Hamilton-Jacobi PDEs 2020 Workshop III: Mean Field Games and Applications "Computational methods for nonlocal mean field games with applications" Levon Nurbekyan - University of California, Los Angeles (UCLA) Abstract: We introduce a novel framework to model and solve me

From playlist High Dimensional Hamilton-Jacobi PDEs 2020

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Numerical Homogenization Approaches for Nonlinear Problems by Barbara Verfürth

DISCUSSION MEETING Multi-Scale Analysis: Thematic Lectures and Meeting (MATHLEC-2021, ONLINE) ORGANIZERS: Patrizia Donato (University of Rouen Normandie, France), Antonio Gaudiello (Università degli Studi di Napoli Federico II, Italy), Editha Jose (University of the Philippines Los Baño

From playlist Multi-scale Analysis: Thematic Lectures And Meeting (MATHLEC-2021) (ONLINE)

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matrix choose a matrix

matrix choose a matrix. Calculating the number of matrix combinations of a matrix, using techniques from linear algebra like diagonalization, eigenvalues, eigenvectors. Special appearance by simultaneous diagonalizability and commuting matrices. In the end, I mention the general case using

From playlist Eigenvalues

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Numerical integration | Wiener process | Runge–Kutta methods | Convergence of random variables | Equilibrium point | Krylov subspace | Delay differential equation | Dormand–Prince method | Moment (mathematics) | Recursion (computer science) | Dynamical system | Hamiltonian mechanics | Block matrix | Stochastic differential equation | Stochastic process | Numerical methods for ordinary differential equations | Independent and identically distributed random variables | Estimation theory | Stratonovich integral | Ergodicity | Fractional Brownian motion | Floating-point arithmetic | Hermite polynomials | Mathematical statistics | Hyperbolic equilibrium point | Stable manifold | Stiff equation | Ordinary differential equation | Discretization | Realization (probability) | Limit cycle | Symplectic geometry | Normal distribution | Pullback attractor | Taylor series | Harmonic oscillator | Stability theory | Random dynamical system | Hurst exponent | Numerical analysis | Padé approximant | Phase portrait | Rate of convergence | Time series | Variation of parameters | L-stability | Bernoulli distribution | Autonomous system (mathematics)