Skip to search form
Skip to main content
Skip to account menu
Semantic Scholar
Semantic Scholar's Logo
Search 210,197,127 papers from all fields of science
Search
Sign In
Create Free Account
Stochastic optimization
Known as:
Stochastic optimisation
, Stochastic search
Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables…
Expand
Wikipedia
(opens in a new tab)
Create Alert
Alert
Related topics
Related topics
48 relations
AIMMS
CMA-ES
Constructive cooperative coevolution
Cross-entropy method
Expand
Broader (1)
Estimation theory
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
,
Jimmy Ba
International Conference on Learning…
2014
Corpus ID: 6628106
We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive…
Expand
Highly Cited
2014
Highly Cited
2014
Efficient mini-batch training for stochastic optimization
Mu Li
,
T. Zhang
,
Yuqiang Chen
,
Alex Smola
Knowledge Discovery and Data Mining
2014
Corpus ID: 1809834
Stochastic gradient descent (SGD) is a popular technique for large-scale optimization problems in machine learning. In order to…
Expand
Highly Cited
2012
Highly Cited
2012
Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization
Zhi Chen
,
Lei Wu
,
Yong Fu
IEEE Transactions on Smart Grid
2012
Corpus ID: 13971451
This paper evaluates the real-time price-based demand response (DR) management for residential appliances via stochastic…
Expand
Highly Cited
2011
Highly Cited
2011
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
John C. Duchi
,
Elad Hazan
,
Y. Singer
Journal of machine learning research
2011
Corpus ID: 538820
We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in…
Expand
Highly Cited
2011
Highly Cited
2011
Distributed delayed stochastic optimization
Alekh Agarwal
,
John C. Duchi
IEEE Conference on Decision and Control
2011
Corpus ID: 901118
We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient…
Expand
Highly Cited
2011
Highly Cited
2011
STOMP: Stochastic trajectory optimization for motion planning
Mrinal Kalakrishnan
,
Sachin Chitta
,
E. Theodorou
,
P. Pastor
,
S. Schaal
IEEE International Conference on Robotics and…
2011
Corpus ID: 16670346
We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on…
Expand
Highly Cited
2011
Highly Cited
2011
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
A. Rakhlin
,
O. Shamir
,
Karthik Sridharan
International Conference on Machine Learning
2011
Corpus ID: 15824822
Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic optimization problems which arise in machine…
Expand
Highly Cited
2008
Highly Cited
2008
Robust Stochastic Approximation Approach to Stochastic Programming
A. Nemirovski
,
A. Juditsky
,
Guanghui Lan
,
A. Shapiro
SIAM Journal on Optimization
2008
Corpus ID: 1767867
In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic…
Expand
Review
2003
Review
2003
Introduction to Stochastic Search and Optimization
J. Spall
2003
Corpus ID: 59958747
From the Publisher: * Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will…
Expand
Highly Cited
1991
Highly Cited
1991
Multi-stage stochastic optimization applied to energy planning
M. Pereira
,
L. Pinto
Mathematical programming
1991
Corpus ID: 206799113
This paper presents a methodology for the solution of multistage stochastic optimization problems, based on the approximation of…
Expand
By clicking accept or continuing to use the site, you agree to the terms outlined in our
Privacy Policy
(opens in a new tab)
,
Terms of Service
(opens in a new tab)
, and
Dataset License
(opens in a new tab)
ACCEPT & CONTINUE