Gradient methods

Coordinate descent

Coordinate descent is an optimization algorithm that successively minimizes along coordinate directions to find the minimum of a function. At each iteration, the algorithm determines a coordinate or coordinate block via a coordinate selection rule, then exactly or inexactly minimizes over the corresponding coordinate hyperplane while fixing all other coordinates or coordinate blocks. A line search along the coordinate direction can be performed at the current iterate to determine the appropriate step size. Coordinate descent is applicable in both differentiable and derivative-free contexts. (Wikipedia).

Coordinate descent
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Plot Points Given as Ordered Pairs on the Coordinate Plane

This video explains how to plot points on the coordinate plane. http://mathispower4u.com

From playlist The Coordinate Plane, Plotting Points, and Solutions to Linear Equations in Two Variables

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Ex: Identifying the Coordinates of Points on the Coordinate Plane

This video explains how to determine the coordinates of points on the coordinate plane. Complete Video List at http://www.mathispower4u.com Search by Topic at http://www.mathispower4u.wordpress.com

From playlist The Coordinate Plane, Plotting Points, and Solutions to Linear Equations in Two Variables

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Plotting Points on the Coordinate Plane

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From playlist Graphing Various Functions

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Working with Scale on the Cartesian Plane (L5.2)

This video lesson explains how to determine the coordinate of points on the coordinate plane when the axes have different scales. It also explains how to scale the axes to plot ordered pairs. Content created by Jenifer Bohart and Amy Volpe from Scottsdale CC (License CC-BY-SA 4.0)

From playlist Functions

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The Coordinate Plane

This video is about The Coordinate Plane

From playlist Integers and The Coordinate Plane

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Determine Points that are Symmetrical Across the x-axis, y-axis, and the Origin

This video provides an example of how to determine two points that are symmetric across the x-axis, y-axis, and the origin. Site: http://mathispower4u.com

From playlist Determining Odd and Even Functions

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Finding the midpoint between two coordinate points ex 1

👉 Learn how to find the midpoint between two points. The midpoint between two points is the point halfway the line joining two given points in the coordinate plane. To find the midpoint between two points we add the x-coordinates of the two given points and divide the result by 2. This giv

From playlist Points Lines and Planes

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Stochastic Gradient Descent and Machine Learning (Lecture 2) by Praneeth Netrapalli

PROGRAM: BANGALORE SCHOOL ON STATISTICAL PHYSICS - XIII (HYBRID) ORGANIZERS: Abhishek Dhar (ICTS-TIFR, India) and Sanjib Sabhapandit (RRI, India) DATE & TIME: 11 July 2022 to 22 July 2022 VENUE: Madhava Lecture Hall and Online This school is the thirteenth in the series. The schoo

From playlist Bangalore School on Statistical Physics - XIII - 2022 (Live Streamed)

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Gradient Origin Networks (Paper Explained w/ Live Coding)

Neural networks for implicit representations, such as SIRENs, have been very successful at modeling natural signals. However, in the classical approach, each data point requires its own neural network to be fit. This paper extends implicit representations to an entire dataset by introducin

From playlist Papers Explained

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Lecture 15 | Convex Optimization I (Stanford)

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how unconstrained minimization can be used in electrical engineering and convex optimization for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizi

From playlist Lecture Collection | Convex Optimization

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How to Escape Saddle Points Efficiently by Praneeth Netrapalli

DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n

From playlist Statistical Physics of Machine Learning 2020

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Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.

The main ideas behind Backpropagation are super simple, but there are tons of details when it comes time to implementing it. This video shows how to optimize three parameters in a Neural Network simultaneously and introduces some Fancy Notation. NOTE: This StatQuest assumes that you alrea

From playlist StatQuest

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Mikhail Belkin: "Optimization for over-parameterized systems of non-linear equations"

High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning "Optimization for over-parameterized systems of non-linear equations: the lessons of deep learning" Mikhail Belkin - Ohio State University Abstract: The success of deep learning is

From playlist High Dimensional Hamilton-Jacobi PDEs 2020

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Finding the midpoint between two coordinate points ex 2

👉 Learn how to find the midpoint between two points. The midpoint between two points is the point halfway the line joining two given points in the coordinate plane. To find the midpoint between two points we add the x-coordinates of the two given points and divide the result by 2. This giv

From playlist Points Lines and Planes

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Gradient descent for the point vortex model - Emma Suckling

PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod

From playlist Data Assimilation Research Program

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How to determine the midpoint between two points on a coordinate axis

👉 Learn how to find the midpoint between two points. The midpoint between two points is the point halfway the line joining two given points in the coordinate plane. To find the midpoint between two points we add the x-coordinates of the two given points and divide the result by 2. This giv

From playlist Points Lines and Planes

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Neural Networks Pt. 2: Backpropagation Main Ideas

Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are quite simple, but there are tons of details. This StatQuest focuses on explaining the main ideas in a way that is easy to understand. NOTE: This StatQuest assumes that you

From playlist StatQuest

Related pages

Support vector machine | Line search | Pseudocode | Coordinate system | Mathematical optimization | Newton's method in optimization | Gradient descent | Stochastic gradient descent | Non-negative matrix factorization | Smoothness | Adaptive coordinate descent | Stationary point