Convex analysis | Convex geometry
Convex analysis is the branch of mathematics devoted to the study of properties of convex functions and convex sets, often with applications in convex minimization, a subdomain of optimization theory. (Wikipedia).
What Is Mathematical Optimization?
A gentle and visual introduction to the topic of Convex Optimization. (1/3) This video is the first of a series of three. The plan is as follows: Part 1: What is (Mathematical) Optimization? (https://youtu.be/AM6BY4btj-M) Part 2: Convexity and the Principle of (Lagrangian) Duality (
From playlist Convex Optimization
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
Convexity and The Principle of Duality
A gentle and visual introduction to the topic of Convex Optimization (part 2/3). In this video, we give the definition of convex sets, convex functions, and convex optimization problems. We also present a beautiful and extremely useful notion in convexity optimization, which is the princ
From playlist Convex Optimization
Lecture 7 | Convex Optimization I
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, expands upon his previous lectures on convex optimization problems for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization pro
From playlist Lecture Collection | Convex Optimization
Lecture 6 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on convex optimization problems for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that ar
From playlist Lecture Collection | Convex Optimization
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
Kazuo Murota: Discrete Convex Analysis (Part 3)
The lecture was held within the framework of the Hausdorff Trimester Program: Combinatorial Optimization
From playlist HIM Lectures 2015
Suvrit Sra: Lecture series on Aspects of Convex, Nonconvex, and Geometric Optimization (Lecture 2)
The lecture was held within the framework of the Hausdorff Trimester Program "Mathematics of Signal Processing". (26.1.2016)
From playlist HIM Lectures: Trimester Program "Mathematics of Signal Processing"
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
Geodesically Convex Optimization (or, can we prove P!=NP using gradient descent) - Avi Wigderson
Computer Science/Discrete Mathematics Seminar II Topic: Geodesically Convex Optimization (or, can we prove P!=NP using gradient descent) Speaker: Avi Wigderson Affiliation: Herbert H. Maass Professor, School of Mathematics Date: April 21, 2020 For more video please visit http://video.ias
From playlist Mathematics
Ohad Shamir - Trade-offs in Distributed Learning
In many large-scale applications, learning must be done on training data which is distributed across multiple machines. This presents an important challenge, with multiple trade-offs between optimization accuracy, statistical performance, communication
From playlist Schlumberger workshop - Computational and statistical trade-offs in learning
Lecture 1 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, gives the introductory lecture for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Con
From playlist Lecture Collection | Convex Optimization
Jürgen Jost (8/29/21): Geometry and Topology of Data
Data sets are often equipped with distances between data points, and thereby constitute a discrete metric space. We develop general notions of curvature that capture local and global properties of such spaces and relate them to topological concepts such as hyperconvexity. This also leads t
From playlist Beyond TDA - Persistent functions and its applications in data sciences, 2021
Suvrit Sra: Lecture series on Aspects of Convex, Nonconvex, and Geometric Optimization (Lecture 3)
The lecture was held within the framework of the Hausdorff Trimester Program "Mathematics of Signal Processing". (28.1.2016)
From playlist HIM Lectures: Trimester Program "Mathematics of Signal Processing"
Parvaneh Joharinad (7/27/22): Curvature of data
Abstract: How can one determine the curvature of data and how does it help to derive the salient structural features of a data set? After determining the appropriate model to represent data, the next step is to derive the salient structural features of data based on the tools available for
From playlist Applied Geometry for Data Sciences 2022
Lecture 13 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on geometric problems for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in eng
From playlist Lecture Collection | Convex Optimization
Jan Giesselmann: Relative entropy for the Euler-Korteweg system with non-monotone pressure
In this joint work with Athanasios Tzavaras (KAUST) and Corrado Lattanzio (L’Aquila) we develop a relative entropy framework for Hamiltonian flows that in particular covers the Euler-Korteweg system, a well-known diffuse interface model for compressible multiphase flows. We put a particula
From playlist Analysis and its Applications
