Add to ORKGStochastic approximations is a rich branch of probability theory and has a wide range of application. Here we study stochastic approximations from the perspective of gradient descent. An important question is what is the asymptotic limit of a stochastic approximation. In that spirit we will provide a detailed description for the limiting behavior of certain one dimensional stochastic approximations
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impa...
Stochastic approximation (SA) is a classical algorithm that has had since the early days a huge impa...
In this article, a family of SDEs are derived as a tool to understand the behavior of numerical opti...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Abstract. Stochastic-approximation gradient methods are attractive for large-scale convex optimizati...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
We consider a stochastic gradient process, which is a special case of stochastic approximation proce...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
We develop the mathematical foundations of the stochastic modified equations (SME) framework for ana...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impa...
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impa...
Stochastic approximation (SA) is a classical algorithm that has had since the early days a huge impa...
In this article, a family of SDEs are derived as a tool to understand the behavior of numerical opti...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Abstract. Stochastic-approximation gradient methods are attractive for large-scale convex optimizati...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
We consider a stochastic gradient process, which is a special case of stochastic approximation proce...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
We develop the mathematical foundations of the stochastic modified equations (SME) framework for ana...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impa...
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impa...
Stochastic approximation (SA) is a classical algorithm that has had since the early days a huge impa...
In this article, a family of SDEs are derived as a tool to understand the behavior of numerical opti...