The multiplicative weights update method is an algorithmic technique most often used for decision making and prediction, and it is also widely used in game theory and algorithm design. Its simplest use case is the problem of prediction from expert advice, where a decision maker must repeatedly choose which expert's advice to follow; the method assigns each expert an initial weight and then updates the weights multiplicatively, reducing an expert's weight after poor performance and increasing it after good performance. It has been discovered independently in several different fields, including machine learning, optimization, theoretical computer science and game theory.
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Source Wikipedia: Multiplicative weight update method
Uses Design Technique
Source Wikipedia: Multiplicative weight update method
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Wikipedia: Multiplicative weight update method
Lead section
The multiplicative weights update method is an algorithmic technique most commonly used for decision making and prediction, and also widely deployed in game theory and algorithm design.
Uses Design Technique: Randomized Algorithms, Article body
In this randomized algorithm, α β → 1 {\displaystyle \alpha _{\beta }\rightarrow 1} if β → 1 {\displaystyle \beta \rightarrow 1} .
In Field: Algorithms and Complexity Theory, Lead sentence
multiplicative weights update method is an algorithmic technique most commonly used for decision making and prediction, and also widely deployed in game theory and algorithm design.
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