Mathematical optimization | Algorithms
Online optimization is a field of optimization theory, more popular in computer science and operations research, that deals with optimization problems having no or incomplete knowledge of the future (online). These kind of problems are denoted as online problems and are seen as opposed to the classical optimization problems where complete information is assumed (offline). The research on online optimization can be distinguished into online problems where multiple decisions are made sequentially based on a piece-by-piece input and those where a decision is made only once. A famous online problem where a decision is made only once is the Ski rental problem. In general, the output of an online algorithm is compared to the solution of a corresponding offline algorithm which is necessarily always optimal and knows the entire input in advance (competitive analysis). In many situations, present decisions (for example, resources allocation) must be made with incomplete knowledge of the future or distributional assumptions on the future are not reliable. In such cases, online optimization can be used, which is different from other approaches such as robust optimization, stochastic optimization and Markov decision processes. (Wikipedia).
[Calculus] Optimization 2 || Lecture 35
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From playlist Calculus 1
A very basic overview of optimization, why it's important, the role of modeling, and the basic anatomy of an optimization project.
From playlist Optimization
[Calculus] Optimization 1 || Lecture 34
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From playlist Calculus 1
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From playlist Advanced Calculus / Multivariable Calculus
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From playlist Advanced Calculus / Multivariable Calculus
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From playlist Digital Marketing Playlist [2023 Updated]🔥 | Digital Marketing Course | Digital Marketing Tutorial For Beginners | Simplilearn
Discrete multi-fidelity optimization
This video is #9 in the Adaptive Experimentation series presented at the 18th IEEE Conference on eScience in Salt Lake City, UT (October 10-14, 2022). In this video, Sterling Baird @sterling-baird presents on discrete multi-fidelity optimization. In discrete multi-fidelity optimization, t
From playlist Optimization tutorial
Learning Minimax Estimators Via Online Learning by Praneeth Netrapalli
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
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DDPS | Model reduction with adaptive enrichment for large scale PDE constrained optimization
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From playlist Digital Marketing Playlist [2023 Updated]🔥 | Digital Marketing Course | Digital Marketing Tutorial For Beginners | Simplilearn
Online Optimization & Energy - Adam Wierman - 10/25/2019
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(Nearly) Optimal Algorithm for Private Online Learning
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Worldwide Calculus: Optimization
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From playlist Worldwide Single-Variable Calculus for AP®