Numerical Optimization Algorithms: Gradient Descent
In this video we discuss a general framework for numerical optimization algorithms. We will see that this involves choosing a direction and step size at each step of the algorithm. In this vide, we investigate how to choose a direction using the gradient descent method. Future videos discuss how to Topics and timestamps: 0:00 – Introduction 2:30 – General framework for numerical optimization algorithms 18:41 – Gradient descent method 32:05 – Practical issues with gradient descent 36:53 – Summary Lecture notes and code can be downloaded from https://github.com/clum/YouTube/tree/... All Optimization videos in a single playlist ( • Optimization ) #Optimization You can support this channel via Patreon at / christopherwlum . Thank you for your help!
Numerical Optimization Algorithms: Constant and Diminishing Step Size
Constrained Optimization: Linear Programs
Numerical Optimization Algorithms: Step Size Via the Armijo Rule
Olympiad level counting (Generating functions)
Introduction to Optimization
Gradient Descent, Step-by-Step
Converting Constrained Optimization to Unconstrained Optimization Using the Penalty Method
Intro to Gradient Descent || Optimizing High-Dimensional Equations
Introduction to Bode Plots
How AI Cracked the Protein Folding Code and Won a Nobel Prize
Computing Euler Angles: The Euler Kinematical Equations and Poisson’s Kinematical Equations
Understanding Lagrange Multipliers Visually
Numerical Optimization Algorithms: Step Size Via Line Minimization
Weird Things Happen When Energy Goes Negative
Trimming a Model of a Dynamic System Using Numerical Optimization
Group theory, abstraction, and the 196,883-dimensional monster
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Unconstrained Optimization
