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
Converting Constrained Optimization to Unconstrained Optimization Using the Penalty Method
In this video we show how to convert a constrained optimization problem into an approximately equivalent unconstrained optimization problem using the penalty method. Topics and timestamps: 0:00 – Introduction 3:00 – Equality constrained only problem 12:50 – Reformulate as approximate unco
From playlist 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
Solving Systems of Equations Using the Optimization Penalty Method
In this video we show how to solve a system of equations using numerical optimization instead of analytically solving. We show that this can be applied to either fully constrained or over constrained problems. In addition, this can be used to solve a system of equations that include both
From playlist Optimization
Calculus 3 Lecture 13.9: Constrained Optimization with LaGrange Multipliers
Calculus 3 Lecture 13.9: Constrained Optimization with LaGrange Multipliers: How to use the Gradient and LaGrange Multipliers to perform Optimization, with constraints, on Multivariable Functions.
From playlist Calculus 3 (Full Length Videos)
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
Calculus: We present a procedure for solving word problems on optimization using derivatives. Examples include the fence problem and the minimum distance from a point to a line problem.
From playlist Calculus Pt 1: Limits and Derivatives
Constrained Optimization: Linear Programs
In this video we introduce the concept of linear optimization problems, AKA linear programs (LPs). LPs are optimization problems where the cost function and constraints are linear (or affine). We examine several examples of linear programs, discuss how to transform general LPs into stand
From playlist Optimization
What in the world is a linear program?
What is a linear program and why do we care? Today I’m going to introduce you to the exciting world of optimization, which is the mathematical field of maximizing or minimizing an objective function subject to constraints. The most fundamental topic in optimization is linear programming,
From playlist Summer of Math Exposition 2 videos
Physical Modeling Tutorial, Part 11: Design Optimization
Learn what Simulink Design Optimization™ is and how to select and design parameters, set requirements or design goals, and optimize model parameters. - Enter the MATLAB and Simulink Racing Lounge: http://bit.ly/2HhcXnU - Download Example Files: Physical Modeling for Formula Student: htt
From playlist Physical Modeling Tutorials
Stanford ENGR108: Introduction to Applied Linear Algebra | 2020 | Lecture 53-VMLS cstrd nonlinear LS
Professor Stephen Boyd Samsung Professor in the School of Engineering Director of the Information Systems Laboratory To follow along with the course schedule and syllabus, visit: https://web.stanford.edu/class/engr108/ To view all online courses and programs offered by Stanford, visit:
From playlist Stanford ENGR108: Introduction to Applied Linear Algebra —Vectors, Matrices, and Least Squares
An introduction to the constrained optimization problems. Lagrangian, Lagrange multipliers, and Karush Kuhn Tucker conditions.
From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)
Can't understand Lagrange Multipliers??
We discuss the idea behind Lagrange Multipliers, why they work, as well as why and when they are useful. External Images Used: 1. https://www.greenbelly.co/pages/contour-lines 2. https://mathoverflow.net/questions/1977/why-is-the-gradient-normal Further Reading: 1. The Variational Pri
From playlist Summer of Math Exposition Youtube Videos
12. Constrained Optimization; Equality Constraints and Lagrange Multipliers
MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015 View the complete course: http://ocw.mit.edu/10-34F15 Instructor: James Swan Students continued to learn how to solve optimization problems that include equality constraints and inequality constraints, as well as the L
From playlist MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015
Paris Perdikaris: "Overcoming gradient pathologies in constrained neural networks"
Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Overcoming gradient pathologies in constrained neural networks" Paris Perdikaris - University of Penns
From playlist Machine Learning for Physics and the Physics of Learning 2019
Spectral properties of steplength selections in gradient (...) - Zanni - Workshop 1 - CEB T1 2019
Zanni (Univ. Modena) / 08.02.2019 Spectral properties of steplength selections in gradient methods: from unconstrained to constrained optimization The steplength selection strategies have a remarkable effect on the efficiency of gradient-based methods for both unconstrained and constrai
From playlist 2019 - T1 - The Mathematics of Imaging
Franca Hoffmann: Covariance-modulated optimal transport
HYBRID EVENT Recorded during the meeting " Probability/PDE Interactions: Interface Models and Particle Systems " the April 25, 2022 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by world
From playlist Dynamical Systems and Ordinary Differential Equations
Lecture: Unconstrained Optimization (Derivative-Free Methods)
We introduce some of the basic techniques of optimization that do not require derivative information from the function being optimized, including golden section search and successive parabolic interpolation.
From playlist Beginning Scientific Computing