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
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C34 Expanding this method to higher order linear differential equations
I this video I expand the method of the variation of parameters to higher-order (higher than two), linear ODE's.
From playlist Differential Equations
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
Bistra Dilkina - Machine Learning for MIP Solving - IPAM at UCLA
Recorded 27 February 2023. Bistra Dilkina of the University of Southern California presents "Machine Learning for MIP Solving" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/artificial-intelligence-and
From playlist 2023 Artificial Intelligence and Discrete Optimization
Differential Equations with Forcing: Method of Variation of Parameters
This video solves externally forced linear differential equations with the method of variation of parameters. This approach is extremely powerful. The idea is to solve the unforced, or "homogeneous" system, and then to replace the unknown coefficients c_k with unknown functions of time c
From playlist Engineering Math: Differential Equations and Dynamical Systems
11_3_1 The Gradient of a Multivariable Function
Using the partial derivatives of a multivariable function to construct its gradient vector.
From playlist Advanced Calculus / Multivariable Calculus
Jonas Witt: Dantzig Wolfe Reformulations for the Stable Set Problem
Dantzig-Wolfe reformulation of an integer program convexifies a subset of the constraints, which yields an extended formulation with a potentially stronger linear programming (LP) relaxation than the original formulation. This paper is part of an endeavor to understand the strength of such
From playlist HIM Lectures: Trimester Program "Combinatorial Optimization"
Lecture 7 | Machine Learning (Stanford)
Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/zJX/ Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. This cours
From playlist Lecture Collection | Machine Learning
An SDCA-powered inexact dual augmented Lagrangian method(...) - Obozinski - Workshop 3 - CEB T1 2019
Guillaume Obozinski (Swiss Data Science Center) / 02.04.2019 An SDCA-powered inexact dual augmented Lagrangian method for fast CRF learning I'll present an efficient dual augmented Lagrangian formulation to learn conditional random field (CRF) models. The algorithm, which can be interpr
From playlist 2019 - T1 - The Mathematics of Imaging
12_2_1 Taylor Polynomials of Multivariable Functions
Now we expand the creation of a Taylor Polynomial to multivariable functions.
From playlist Advanced Calculus / Multivariable Calculus
Visualizing Data using t-SNE (algorithm) | AISC Foundational
Toronto Deep Learning Series, 1 November 2018 Paper Review: http://www.jmlr.org/papers/v9/vandermaaten08a.html Speaker: Sabyasachi Dasgupta (University of Toronto) Host: Statflo Date: Nov 1st, 2018 Visualizing Data using t-SNE We present a new technique called "t-SNE" that visualizes
From playlist Math and Foundations
Stanford Seminar - Designing to Empower Marginalized Communities through Social Technology
Alexandra To Northeastern University October 30, 2020 Technology frequently marginalizes people from underrepresented and vulnerable groups; more and more, we're learning how social media platforms, AI systems, machine learning algorithms, video games, etc., can enact, amplify, or perpetu
From playlist Stanford Seminars
Lexing Ying: "Strictly-correlated Electron Functional and Multimarginal Optimal Transport"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop II: Tensor Network States and Applications "Strictly-correlated Electron Functional and Multimarginal Optimal Transport" Lexing Ying - Stanford University, Mathematics Abstract: We introduce methods
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Mathematical and Computational Aspects of Machine Learning - 10 October 2019
http://www.crm.sns.it/event/451/timetable.html#title 9:00- 10:00 Barbier, Jean Mean field theory of high-dimensional Bayesian inference 10:00- 10:30 Coffee break 10:30- 11:30 Peyré, Gabriel Optimal Transport for Data Science 11:30- 12:30 Peyré, Gabriel Optimal Transport for Data Scien
From playlist Centro di Ricerca Matematica Ennio De Giorgi
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
Seffi Naor: Recent Results on Maximizing Submodular Functions
I will survey recent progress on submodular maximization, both constrained and unconstrained, and for both monotone and non-monotone submodular functions. The lecture was held within the framework of the Hausdorff Trimester Program: Combinatorial Optimization.
From playlist HIM Lectures 2015
Algorithmic Thresholds for Mean-Field Spin Glasses - Mark Sellke
Probability Seminar Topic: Algorithmic Thresholds for Mean-Field Spin Glasses Speaker: Mark Sellke Affiliation: Member, School of Mathematics Date: September 23, 2022 I will explain recent progress on computing approximate ground states of mean-field spin glass Hamiltonians, which are ce
From playlist Mathematics
Bistra Dilkina: "Decision-focused learning: integrating downstream combinatorics in ML"
Deep Learning and Combinatorial Optimization 2021 "Decision-focused learning: integrating downstream combinatorics in ML" Bistra Dilkina - University of Southern California (USC) Abstract: Closely integrating ML and discrete optimization provides key advantages in improving our ability t
From playlist Deep Learning and Combinatorial Optimization 2021
Solve a System of Equations Using Elimination with Fractions
👉Learn how to solve a system (of equations) by elimination. A system of equations is a set of equations which are collectively satisfied by one solution of the variables. The elimination method of solving a system of equations involves making the coefficient of one of the variables to be e
From playlist Solve a System of Equations Using Elimination | Hard