Optimization algorithms and methods
Iterated Local Search (ILS) is a term in applied mathematics and computer sciencedefining a modification of local search or hill climbing methods for solving discrete optimization problems. Local search methods can get stuck in a local minimum, where no improving neighbors are available. A simple modification consists of iterating calls to the local search routine, each time starting from a different initial configuration. This is called repeated local search, and implies that the knowledge obtained during the previous local search phases is not used. Learning implies that the previous history, for example the memory about the previously found local minima, is mined to produce better and better starting points for local search. The implicit assumption is that of a clustered distribution of local minima: when minimizing a function, determining good local minima is easier when starting from a local minimum with a low value than when starting from a random point. The only caveat is to avoid confinement in a given attraction basin, so that the kick to transform a local minimizer into the starting point for the next run has to be appropriately strong, but not too strong to avoid reverting to memory-less random restarts. Iterated Local Search is based on building a sequence of locally optimal solutions by: 1. * perturbing the current local minimum; 2. * applying local search after starting from the modified solution. The perturbation strength has to be sufficient to lead the trajectory to a different attraction basin leading to a different local optimum. (Wikipedia).
Conducting an Online Job Search
In this video, you’ll learn more about conducting an online job search. Visit https://www.gcflearnfree.org/jobsearchandnetworking/find-a-job-online/1/ to learn even more. We hope you enjoy!
From playlist Searching for a Job
How to Do Local SEO: Complete A-Z Tutorial
This video will show you how to do local SEO to get free traffic from organic search and Google Maps listings using Google My Business and search engine optimization. Optimizing your website for Google local search is different than for a global company. While there is some overlap betwee
From playlist Local SEO Tutorials for Small Business
In this video, you’ll learn some tips and tricks for getting the most out of using Google to search for stuff online. Visit https://edu.gcfglobal.org/en/searchbetter/google-search-tips/1/ to learn even more. We hope you enjoy!
From playlist Search Better
In this video, you’ll learn more about using the Internet to search for a home online. Visit https://www.gcflearnfree.org/using-the-web-to-get-stuff-done/searching-for-a-home-online/1/ for our text-based lesson. This video includes information on: • Tools to use to search for a home onlin
From playlist Using the Web to Get Stuff Done
Finite Difference Method for finding roots of functions including an example and visual representation. Also includes discussions of Forward, Backward, and Central Finite Difference as well as overview of higher order versions of Finite Difference. Chapters 0:00 Intro 0:04 Secant Method R
From playlist Root Finding
In this video, you’ll learn more about using basic search strategies online. Visit https://www.gcflearnfree.org/searchbetter/google-search-tips/2/ for our text-based lesson. This video includes information on: • Using basic search strategies to find information on Google We hope you enjo
From playlist Internet Tips
Indexing 17: distributed search
Instead of using MapReduce to construct a single index, we can distribute portions of the index across a cluster of machines. We can then send a query to all the machines, receive partial ranked lists and then combine them into one list that would be returned to the user. This is known as
From playlist IR7 Inverted Indexing
Nelder-Mead Downhill Simplex Method (2 dimensions) + A numerical Example
This video is about Nelder-Mead Downhill Simplex Method (2 dimensions) + A numerical Example
From playlist Optimization
Constraint Satisfaction Problems (CSPs) 7 - Local Search | Stanford CS221: AI (Autumn 2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Optimisation - an introduction: Professor Coralia Cartis, University of Oxford
Coralia Cartis (BSc Mathematics, Babesh-Bolyai University, Romania; PhD Mathematics, University of Cambridge (2005)) has joined the Mathematical Institute at Oxford and Balliol College in 2013 as Associate Professor in Numerical Optimization. Previously, she worked as a research scientist
From playlist Data science classes
Symbolic Regression and Program Induction: Lars Buesing
Machine Learning for the Working Mathematician: Week Fourteen 2 June 2022 Lars Buesing, Searching for Formulas and Algorithms: Symbolic Regression and Program Induction Abstract: In spite of their enormous success as black box function approximators in many fields such as computer vision
From playlist Machine Learning for the Working Mathematician
Particle Swarm Optimization - Part 5: Veclocity Clamping
This video is about Particle Swarm Optimization - Part 5: Veclocity Clamping
From playlist Optimization
Jorge Nocedal: "Tutorial on Optimization Methods for Machine Learning, Pt. 1"
Graduate Summer School 2012: Deep Learning, Feature Learning "Tutorial on Optimization Methods for Machine Learning, Pt. 1" Jorge Nocedal, Northwestern University Institute for Pure and Applied Mathematics, UCLA July 19, 2012 For more information: https://www.ipam.ucla.edu/programs/summ
From playlist GSS2012: Deep Learning, Feature Learning
Nexus trimester - David Gamarnik (MIT)
(Arguably) Hard on Average Optimization Problems and the Overlap Gap Property David Gamarnik (MIT) March 17, 2016 Abstract: Many problems in the area of random combinatorial structures and high-dimensional statistics exhibit an apparent computational hardness, even though the formal resu
From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
Particle Swarm Optimization - Part 3: Local Best PSO
This video is about Particle Swarm Optimization - Part 3: Local Best PSO
From playlist Optimization
Player of Games: All the games, one algorithm! (w/ author Martin Schmid)
#playerofgames #deepmind #alphazero Special Guest: First author Martin Schmid (https://twitter.com/Lifrordi) Games have been used throughout research as testbeds for AI algorithms, such as reinforcement learning agents. However, different types of games usually require different solution
From playlist Papers Explained
M. Grazia Speranza: "Fundamentals of optimization" (Part 2/2)
Watch part 1/2 here: https://youtu.be/VdKija5AXOk Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials 2020 "Fundamentals of optimization" (Part 2/2) M. Grazia Speranza - University of Brescia Institute for Pure and Applied Mathematics, UCLA September 23, 2020 Fo
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
Top 50 ITIL Interview Questions And Answers | ITIL Foundation Certification Training | Simplilearn
🔥 ITIL® 4 Foundation Certification Training Course: https://www.simplilearn.com/it-service-management/itil-foundation-training?utm_campaign=ITILIQsJan25-aJyVlAV2xyY&utm_medium=Descriptionff&utm_source=youtube This tutorial on Top 50 ITIL interview questions and answers has the top 50 inte
From playlist ITIL Training Videos [2022 Updated]