Kazuo Murota: Discrete Convex Analysis (Part 2)
The lecture was held within the framework of the Hausdorff Trimester Program: Combinatorial Optimization
From playlist HIM Lectures 2015
Define a linear function. Determine if a linear function is increasing or decreasing. Interpret linear function models. Determine linear functions. Site: http://mathispower4u.com
From playlist Introduction to Functions: Function Basics
Define linear functions. Use function notation to evaluate linear functions. Learn to identify linear function from data, graphs, and equations.
From playlist Algebra 1
What are bounded functions and how do you determine the boundness
👉 Learn about the characteristics of a function. Given a function, we can determine the characteristics of the function's graph. We can determine the end behavior of the graph of the function (rises or falls left and rises or falls right). We can determine the number of zeros of the functi
From playlist Characteristics of Functions
Introduction to Linear Functions and Slope (L10.1)
This lesson introduces linear functions, describes the behavior of linear function, and explains how to determine the slope of a line given two points. Video content created by Jenifer Bohart, William Meacham, Judy Sutor, and Donna Guhse from SCC (CC-BY 4.0)
From playlist Introduction to Functions: Function Basics
When is a function bounded below?
👉 Learn about the characteristics of a function. Given a function, we can determine the characteristics of the function's graph. We can determine the end behavior of the graph of the function (rises or falls left and rises or falls right). We can determine the number of zeros of the functi
From playlist Characteristics of Functions
Ex: Determine if a Linear Function is Increasing or Decreasing
This video explains how to determine if a linear function is increasing or decreasing. The results are discussed graphically. Site: http://mathispower4u.com
From playlist Introduction to Functions: Function Basics
Kazuo Murota: Extensions and Ramifications of Discrete Convexity Concepts
Submodular functions are widely recognized as a discrete analogue of convex functions. This convexity view of submodularity was established in the early 1980's by the fundamental works of A. Frank, S. Fujishige and L. Lovasz. Discrete convex analysis extends this view to broader classes of
From playlist HIM Lectures 2015
Matthew Kennedy: Noncommutative convexity
Talk by Matthew Kennedy in Global Noncommutative Geometry Seminar (Europe) http://www.noncommutativegeometry.nl/ncgseminar/ on May 5, 2021
From playlist Global Noncommutative Geometry Seminar (Europe)
Lecture 21: Minimizing a Function Step by Step
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k In this lecture, P
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Ramon van Handel: The mysterious extremals of the Alexandrov-Fenchel inequality
The Alexandrov-Fenchel inequality is a far-reaching generalization of the classical isoperimetric inequality to arbitrary mixed volumes. It is one of the central results in convex geometry, and has deep connections with other areas of mathematics. The characterization of its extremal bodie
From playlist Trimester Seminar Series on the Interplay between High-Dimensional Geometry and Probability
Stein Structures: Existence and Flexibility - Kai Cieliebak
Kai Cieliebak Ludwig-Maximilians-Universitat, Munich, Germany March 2, 2012
From playlist Mathematics
Stein Structures: Existence and Flexibility - Kai Cieliebak
Kai Cieliebak Ludwig-Maximilians-Universitat, Munich, Germany March 1, 2012
From playlist Mathematics
Andrea Colesanti: An overview on a young research topic: valuations on spaces of functions
I will start from the theory of valuations on convex bodies, which for me was the main motivation to study corresponding functionals in an analytic setting. Then I will devote some time to the notion of valuations on a space of functions. After a general review on this topic, I will descri
From playlist Trimester Seminar Series on the Interplay between High-Dimensional Geometry and Probability
Bo'az Klartag - Convexity in High Dimensions I
October 28, 2022 This is the first talk in the Minerva Mini-course of Bo'az Klartag, Weizmann Institute of Science and Princeton's Fall 2022 Minerva Distinguished Visitor We will discuss recent progress in the understanding of the isoperimetric problem for high-dimensional convex sets, an
From playlist Minerva Mini Course - Bo'az Klartag
Jean-Bernard Lasserre: The moment-LP and moment-SOS approaches
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 Control Theory and Optimization
Stephen Wright: "Some Relevant Topics in Optimization, Pt. 2"
Graduate Summer School 2012: Deep Learning Feature Learning "Some Relevant Topics in Optimization, Pt. 2" Stephen Wright, University of Wisconsin-Madison Institute for Pure and Applied Mathematics, UCLA July 16, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-school
From playlist GSS2012: Deep Learning, Feature Learning
Determine if a Function is a Polynomial Function
This video explains how to determine if a function is a polynomial function. http://mathispower4u.com
From playlist Determining the Characteristics of Polynomial Functions
Lecture 2 | Convex Optimization I (Stanford)
Guest Lecturer Jacob Mattingley covers convex sets and their applications in electrical engineering and beyond for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex se
From playlist Lecture Collection | Convex Optimization