A Random Walk Approach to First-Order Stochastic Convex Optimization

Date July 7, 2019
Authors Sattar Vakili, Qing Zhao (Cornell University)

An active search strategy based on devising a biased random walk on an infinite-depth tree constructed through successive partitioning of the search domain is developed. By localizing data processing to small subsets of the input domain based on the tree structure, it enjoys very low computation and memory complexity and allows dynamic allocation of limited data storage.

View the paper

Share
,,

Related articles

What is Stochastic Network Control?

Labs
Features

3 reasons why machine learning projects fail - and how to avoid them

Insights
Features

Soft Q-Learning with Mutual-Information Regularization

Labs
Paper
Decision Engine
    ProductTechnology
Applications
Labs
Insights
Secondmind
©2020 Secondmind Ltd.