Infinite Limits With Equal Exponents (Calculus)
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From playlist Calculus
13_2 Optimization with Constraints
Here we use optimization with constraints put on a function whose minima or maxima we are seeking. This has practical value as can be seen by the examples used.
From playlist Advanced Calculus / Multivariable Calculus
A very basic overview of optimization, why it's important, the role of modeling, and the basic anatomy of an optimization project.
From playlist Optimization
Epsilon delta limit (Example 3): Infinite limit at a point
This is the continuation of the epsilon-delta series! You can find Examples 1 and 2 on blackpenredpen's channel. Here I use an epsilon-delta argument to calculate an infinite limit, and at the same time I'm showing you how to calculate a right-hand-side limit. Enjoy!
From playlist Calculus
Calculus: Optimization Problems
In this video, I discuss optimization problems. I give an outline for how to approach these kinds of problems and worth through a couple of examples.
From playlist Calculus
13_1 An Introduction to Optimization in Multivariable Functions
Optimization in multivariable functions: the calculation of critical points and identifying them as local or global extrema (minima or maxima).
From playlist Advanced Calculus / Multivariable Calculus
What Is Mathematical Optimization?
A gentle and visual introduction to the topic of Convex Optimization. (1/3) This video is the first of a series of three. The plan is as follows: Part 1: What is (Mathematical) Optimization? (https://youtu.be/AM6BY4btj-M) Part 2: Convexity and the Principle of (Lagrangian) Duality (
From playlist Convex Optimization
Constrained optimization introduction
See a simple example of a constrained optimization problem and start getting a feel for how to think about it. This introduces the topic of Lagrange multipliers.
From playlist Multivariable calculus
Arthur Krener: "Al'brekht’s Method in Infinite Dimensions"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop I: High Dimensional Hamilton-Jacobi Methods in Control and Differential Games "Al'brekht’s Method in Infinite Dimensions" Arthur Krener, Naval Postgraduate School Abstract: Al'brekht's method is a way optimally stabilize a finite dimens
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GftN16 Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Joint IAS/Princeton University Number Theory Seminar - James Maynard
James Maynard Université de Montréal March 6, 2014 For more videos, visit http://video.ias.edu
From playlist Mathematics
Koopman Observable Subspaces & Finite Linear Representations of Nonlinear Dynamics for Control
This video illustrates the use of the Koopman operator to simulate and control a nonlinear dynamical system using a linear dynamical system on an observable subspace. From the Paper: Koopman observable subspaces and finite linear representations of nonlinear dynamical systems for contro
From playlist Research Abstracts from Brunton Lab
Learning Optimal Control with Stochastic Models of Hamiltonian Dynamics for Shape & Function Optim.
Speaker: Chandrajit Bajaj (7/25/22) Abstract: Shape and Function Optimization can be achieved through Optimal Control over infinite-dimensional search space. All optimal control problems can be solved by first applying the Pontryagin maximum principle, and then computing a solution to the
From playlist Applied Geometry for Data Sciences 2022
Ivan Yegorov: "Attenuation of the curse of dimensionality in continuous-time nonlinear optimal f..."
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop I: High Dimensional Hamilton-Jacobi Methods in Control and Differential Games "Attenuation of the curse of dimensionality in continuous-time nonlinear optimal feedback stabilization problems" Ivan Yegorov, North Dakota State University
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Stefan Volkwein: Introduction to PDE-constrained optimization - lecture 1
HYBRID EVENT Recorded during the meeting "Domain Decomposition for Optimal Control Problems" the September 05, 2022 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematici
From playlist Jean-Morlet Chair - Gander/Hubert
Angela Kunoth: 25+ Years of Wavelets for PDEs
Abstract: Ingrid Daubechies' construction of orthonormal wavelet bases with compact support published in 1988 started a general interest to employ these functions also for the numerical solution of partial differential equations (PDEs). Concentrating on linear elliptic and parabolic PDEs,
From playlist Numerical Analysis and Scientific Computing
Anthony Nouy: Adaptive low-rank approximations for stochastic and parametric equations [...]
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Numerical Analysis and Scientific Computing
Limit of a Vector-Valued Function as t approaches Infinity
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Limit of a Vector-Valued Function as t approaches Infinity
From playlist Calculus
Lecture 8 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels. This course provides a broad introduction to machine learning and
From playlist Lecture Collection | Machine Learning