# Adaptive dimensional search

Adaptive dimensional search algorithms differ from nature-inspired metaheuristic techniques in the sense that they do not use any metaphor as an underlying principle for implementation. Rather, they utilize a simple, performance-oriented methodology based on the update of the search dimensionality ratio (SDR) parameter at each iteration. Many robust metaheuristic techniques, such as simulated annealing, evolutionary algorithms, particle swarm optimization, and ant colony optimization, have been introduced by researchers in the last few decades through clearly identifying and formulating similarities between algorithms and the processes they are modeled on. However, over time this trend of developing new search methods has made researchers feel obligated to associate their innovative ideas with some natural event to provide a basis for justification of their thoughts and the originality of their algorithms. As a result, literature has abounded with metaheuristic algorithms that have weak or no similarities to the natural processes which they are purported to derive from. (Wikipedia).

Adding Vectors Geometrically: Dynamic Illustration

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From playlist Trigonometry: Dynamic Interactives!

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From playlist Numerical Methods

How to use trigonometry values to solve a word problem - Learn math online

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Computational Methods for Numerical Relativity, Part 3 Frans Pretorius

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From playlist PiTP 2009

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Choosing Indexes for Similarity Search (Faiss in Python)

Facebook AI Similarity Search (Faiss) is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articlesâ€Š-â€Šwith incredible accuracy in sub-second timescales for billion+ size datasets. The success in Faiss is due to many reasons. One

From playlist Vector Similarity Search and Faiss Course

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Welcome to video #1 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video, Sterling introduces the concept of adaptive experimentation and covers t

From playlist Optimization tutorial

Comparing Bayesian optimization with traditional sampling

Welcome to video #2 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video Sterling introduces Bayesian Optimization as an alternative method for sa

From playlist Optimization tutorial

Approximate nearest neighbor search in high dimensions â€“ Piotr Indyk â€“ ICM2018

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From playlist Mathematical Aspects of Computer Science

Stanford Seminar - Towards Robust Human-Robot Interaction: A Quality Diversity Approach

Stefanos Nikolaidis is an Assistant Professor in computer science at the University of Southern California. This talk was given on March 4, 2022. The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automaticall

AI Weekly Update - March 8th, 2021 (#27)!

Thank you for watching! Please Subscribe! Content Links: Multimodal neurons (OpenAI): https://openai.com/blog/multimodal-neurons/ Multimodal neurons (Distil): https://distill.pub/2021/multimodal-neurons/ DeepDream (Wikipedia): https://en.wikipedia.org/wiki/DeepDream CLIP (OpenAI): https:/

From playlist AI Research Weekly Updates

Designing Nanostructures that Reproduce Colors: an Adaptive Mesh Search Technique

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From playlist Wolfram Technology Conference 2017

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From playlist Mathematics

Anthony Nouy: Adaptive low-rank approximations for stochastic and parametric equations [...]

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 Numerical Analysis and Scientific Computing

AI Weekly Update - April 27th, 2020 (#19)

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How to find the height of a tree given the angle of elevation

đź‘‰ Learn how to solve the word problems with trigonometry. Word problems involving angles, including but not limited to: bearings, angle of elevations and depressions, triangles problems etc are solved using trigonometry. To be able to solve these problems it is important that you have a gr

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## Related pages

Particle swarm optimization | Evolutionary algorithm | Metaheuristic | Ant colony optimization algorithms | Simulated annealing | Algorithm