Continuous vs. Discrete Optimization (CDO)
This describes how to use both data sets and functions together and separately to solve optimization problems! Personal Website: ...
Engineering Demystified
Combinatorial Optimization Part I
CSE-IITKGP NMEICT T10KT
Analytic Combinatorics - 1.2.2 Telescoping
Part 1: Analysis of Algorithms Unit 2: Recurrences Lesson 2 Telescoping Playlist: ...
Bob Trenwith
SGP 2020 Graduate School: libigl short introduction
Alec Jacobson presents a short introduction to libigl – an open source C++ geometry processing library as part of the SGP 2020 Graduate School.
Alec Jacobson
Mathematics of Lattices
Daniele Micciancio (UC San Diego) https://simons.berkeley.edu/talks/basic-mathematics-lattices Lattices: Algorithms, Complexity, and Cryptography Boot Camp.
Simons Institute
Algebraic combinatorics: applications to statistical mechanics and complexity theory - Greta Panova
Short proofs are hard to find (joint work w/ Toni Pitassi and Hao Wei) - Ian Mertz Computer Science/Discrete Mathematics Seminar II Topic: Short proofs are hard ...
Institute for Advanced Study
Binomial Coefficients Asymptotics || @ CMU || Lecture 3c of CS Theory Toolkit
Asymptotics of binomial coefficients, "n choose k", including discussion of the binary entropy function. Lecture 3c of "CS Theory Toolkit": a semester-long ...
Ryan O'Donnell
Stanford Lecture - Don Knuth: The Analysis of Algorithms (2015, recreating 1969)
Known as the Father of Algorithms, Professor Donald Knuth, recreates his very first lecture taught at Stanford Univeristy. Professor Knuth is an American ...
stanfordonline
Donald Knuth: "The Art of Computer Programming: Satisfiability and Combinatorics"
This lecture is hosted by Sorin Istrail and Eli Upfal and a Sweat Box Session featuring rigorous questioning from graduate students and other attendees follows.
Brown University
Introduction To Optimization: Gradients, Constraints, Continuous and Discrete Variables
A brief introduction to the concepts of gradients, constraints, and the differences between continuous and discrete variables. This video is part of an introductory ...
AlphaOpt
Discrete : Reappraising the digital in architecture (November 15, 2019)
Marrikka Trotter introduces a launch of Architectural Design magazine's Profile #258, “Discrete: reappraising the digital in architecture”. She presents a history of ...
SCI-Arc Media Archive
Discrete Optimization with Branch and Bound
Integer, Mixed Integer Linear, and Mixed Integer Nonlinear Programming (IP, MILP, MINLP) problems can be solved with the branch and bound algorithm.
APMonitor.com
Solving Simple Stochastic Optimization Problems with Gurobi
The importance of incorporating uncertainty into optimization problems has always been known; however, both the theory and software were not up to the ...
Gurobi Optimization
Basic Modeling for Discrete Optimization - Arrays and Comprehensions by University of Melbourne #6
This video is part of an online course, Basic Modeling for Discrete Optimization, created by The University of Melbourne and The Chinese University of Hong ...
Coursera
The Knapsack problem in Combinatorial Optimization | Convex Optimization Application # 2
Informally, the problem is to maximize the sum of the values of the items in the knapsack so that the sum of the weights is less than or equal to the knapsack's ...
Ahmad Bazzi
Lec 1: Introduction to Optimization
Computer Aided Applied Single Objective Optimization Course URL: https://swayam.gov.in/nd1_noc20_ch19/preview Prof. Prakash Kotecha Dept. of Chemical ...
NPTEL IIT Guwahati
Introduction to Metaheuristics (1/9)
Playlist at https://www.youtube.com/playlist?list=PLN4kTzLXGGgWNf4CDyoZZOsjOCftW5ej6 Classes for the Degree of Industrial Management Engineering at ...
Luis R. Izquierdo
BRANCH AND BOUND ALGORITHM THEORY 190516
Created using PowToon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free ...
atikah ghazali
Basic Modeling for Discrete Optimization - Module 1 Summary by The University of Melbourne #8
This video is part of an online course, Basic Modeling for Discrete Optimization, created by The University of Melbourne and The Chinese University of Hong ...
Coursera
Learning to Code: Machine Learning for Program Induction
The task of synthesizing programs given only example input-output behaviour is experiencing a surge of interest in the machine learning community. We present ...
Microsoft Research
Integer Linear Programming | 0-1 Binary Constraints | Examples - Part 1
This video shows how to formulate relational/logical constraints using binary or 0-1 integer variables: ~~~~~~~~~~~ This channel does not contain ads. Support ...
Joshua Emmanuel
Latent State Recovery in Reinforcement Learning - John Langford
Seminar on Theoretical Machine Learning Topic: Latent State Recovery in Reinforcement Learning Speaker: John Langford Affiliation: Microsoft Research Date: ...
Institute for Advanced Study
MOOC Intro Discrete Mathematics
MINIRANI S
Julian Hall - High performance computational techniques for the simplex method
Part of CO@Work2020: http://co-at-work.zib.de/ More information on HiGHS: https://www.highs.dev/ Join our Zoom Q&A on Wednesday at 9am CEST and 8pm ...
Mixed Integer Programming
Chapter 2: Linear Programming Models
Quantitative Method for Decision Making: Chapter 2- Linear Programming Models.
Solomon Getachew
Integer Programming: The Global Impact
Integer Programming: The Global Impact: After receiving an overwhelming response from George Nemhauser's plenary talk at EURO-INFORMS, he agreed to ...
GeorgiaTech H. Milton Stewart School of ISyE
CPAIOR 2020 Master Class: Constraint Programming
Master Class Session on CP-SAT from the OR-Tools suite by Laurent Perron and Frédéric Didier Chair: Mohamed Siala.
CPAIOR 2020
Submodularity: Theory and Applications I
Stefanie Jegelka, MIT https://simons.berkeley.edu/talks/andreas-krause-stefanie-jegelka-01-23-2017-1 Foundations of Machine Learning Boot Camp.
Simons Institute
A/B Testing for Game Design Iteration: A Bayesian Approach
In this GDC 2014 session, Swyrve's Steven Collins explains why the Bayesian approach to A/B testing and game design, in comparison to the more traditional ...
GDC
Approximation Algorithms for Discrete Stochastic Optimization Problems
We will survey recent work in the design of approximation algorithms for several discrete stochastic optimization problems, with a particular focus on 2-stage ...
Microsoft Research
Minkowski sums, mixed faces and combinatorial isoperimetry - Adiparsito
Computer Science/Discrete Mathematics Seminar II Topic: Minkowski sums, mixed faces and combinatorial isoperimetry Speaker: Karim Adiprasito Date: ...
Institute for Advanced Study
JuMP-dev 2018 | Systematically building MIP formulations using JuMP and Julia | Joey Huchette
Mixed-integer programming (MIP) has proven itself a valuable tool for practically solving difficult discrete or nonconvex optimization problems. However, the ...
The Julia Programming Language
Optimization course: Discrete optimization
In this video, I give some ideas on Discrete optimization and dynamic programming. I also discuss the travelling salesman and knapsack problem Enjoy :-)
Ahmad Hably
Stanford Lecture: Donald Knuth - "Finding All Spanning Trees" (2003)
Don Knuth's 10th Annual Christmas Tree Lecture December 16, 2003 Professor Knuth is the Professor Emeritus at Stanford University. Dr. Knuth's classic ...
stanfordonline
"The Exascale Computing Project and the Future of HPC " with Doug Kothe
Title: The Exascale Computing Project and the Future of HPC Speaker: Doug Kothe Date: 4/30/19 Abstract The mission of the US Department of Energy (DOE) ...
Association for Computing Machinery (ACM)
Sharp sphere packings – Maryna Viazovska – ICM2018
Number Theory | Combinatorics Invited Lecture 3.1 | 13.1 Sharp sphere packings Maryna Viazovska Abstract: In this talk we will speak about recent progress on ...
Rio ICM2018
RI Seminar: Alec Jacobson : Geometry Processing in The Wild
Alec Jacobson Assistant Professor Department of Computer Science, University of Toronto Geometry Processing in The Wild Abstract: Geometric data abounds, ...
cmurobotics
Randomized Interior Point Methods for Sampling and Optimization
We present a Markov Chain, "Dikin walk", for sampling from a convex body equipped with a self-concordant barrier. This Markov Chain corresponds to a natural ...
Microsoft Research
Quantum Computation for Chemistry and Materials
Dr. Jarrod McClean Google's Quantum Artificial Intelligence Lab Quantum computers promise to dramatically advance our understanding of new materials and ...
HRL Laboratories, LLC
Log-concavity, matroids and expanders - Cynthia Vinzant
Members' Seminar Topic: Log-concavity, matroids and expanders Speaker: Cynthia Vinzant Affiliation: North Carolina State University; von Neumann Fellow, ...
Institute for Advanced Study
MIT CompBio Lecture 08 - Epigenomics I (Fall '19)
MIT Computational Biology: Genomes, Networks, Evolution, Health http://compbio.mit.edu/6.047/ Prof. Manolis Kellis Full playlist with all videos in order is here: ...
Manolis Kellis
Stanford Lecture: Don Knuth—"Hamiltonian Paths in Antiquity" (2016)
Computer Musings 2016 Donald Knuth's 23rd Annual Christmas Tree Lecture: "Hamiltonian Paths in Antiquity" Speaker: Donald Knuth About 1850, William ...
stanfordonline
