# multiplicative weights update algorithm

Published articles for multiplicative weights update algorithm.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## The Reasonable Effectiveness of the Multiplicative Weights Update Algorithm

DevFeed: [The Reasonable Effectiveness of the Multiplicative Weights Update Algorithm](<https://devfeed.tech/articles/the-reasonable-effectiveness-of-the-multiplicative-weights-update-algorithm-40407.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2017/02/27/the-reasonable-effectiveness-of-the-multiplicative-weights-update-algorithm/>)

Published: 2017-02-27T08:00:42Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [game-theory](<https://devfeed.tech/tags/game-theory.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [linear-programming](<https://devfeed.tech/tags/linear-programming.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [multiplicative-weights-update-algorithm](<https://devfeed.tech/tags/multiplicative-weights-update-algorithm.md>), [mwua](<https://devfeed.tech/tags/mwua.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [programming](<https://devfeed.tech/tags/programming.md>), [python](<https://devfeed.tech/tags/python.md>)

### AI overview

This tutorial explains the Multiplicative Weights Update Algorithm (MWUA), including its weight-update rule, an investment-expert example, and the algorithm's theoretical guarantee for identifying a strong expert over repeated rounds. It also introduces an interactive demonstration.

### Source excerpt

papad Hard to believe Sanjeev Arora and his coauthors consider it "a basic tool [that should be] taught to all algorithms students together with divide-and-conquer, dynamic programming, and random sampling." Christos Papadimitriou calls it "so hard to believe that it has been discovered five times and forgotten." It has formed the basis of algorithms in machine learning, optimization, game theory, economics, biology, and more. What mystical algorithm has such broad applications?

## Adversarial Bandits and the Exp3 Algorithm

DevFeed: [Adversarial Bandits and the Exp3 Algorithm](<https://devfeed.tech/articles/adversarial-bandits-and-the-exp3-algorithm-40333.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2013/11/08/adversarial-bandits-and-the-exp3-algorithm/>)

Published: 2013-11-08T09:00:40Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [Learning](<https://devfeed.tech/topics/learning.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [bandit-learning](<https://devfeed.tech/tags/bandit-learning.md>), [bandits](<https://devfeed.tech/tags/bandits.md>), [exp3](<https://devfeed.tech/tags/exp3.md>), [exploitation](<https://devfeed.tech/tags/exploitation.md>), [exploration](<https://devfeed.tech/tags/exploration.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [multiplicative-weights-update-algorithm](<https://devfeed.tech/tags/multiplicative-weights-update-algorithm.md>), [programming](<https://devfeed.tech/tags/programming.md>), [python](<https://devfeed.tech/tags/python.md>)

### AI overview

This article introduces adversarial bandit learning and explains how the Exp3 algorithm addresses decisions with uncertain outcomes. It contrasts adversarial payoffs with the simpler stochastic setting and frames success relative to the best single action over repeated trials.

### Source excerpt

In the last twenty years there has been a lot of research in a subfield of machine learning called Bandit Learning. The name comes from the problem of being faced with a large sequence of slot machines (once called one-armed bandits) each with a potentially different payout scheme. The problems in this field all focus on one central question: If I have many available actions with uncertain outcomes, how should I act to maximize the quality of my results over many trials?