Add to ORKGWe consider Online Convex Optimization (OCO) in the setting where the costs are m-strongly convex and the online learner pays a switching cost for changing decisions between rounds. We show that the recently proposed Online Balanced Descent (OBD) algorithm is constant competitive in this setting, with competitive ratio 3+O(1/m), irrespective of the ambient dimension. We demonstrate the generality of our approach by showing that the OBD framework can be used to construct competitive a algorithm for LQR control
Online optimization has emerged as powerful tool in large scale optimization. In this pa-per, we int...
We aim to design universal algorithms for online convex optimization, which can handle multiple comm...
Online learning and convex optimization algorithms have become essential tools for solving problems ...
We consider Online Convex Optimization (OCO) in the setting where the costs are mm-strongly convex a...
We study Smoothed Online Convex Optimization, a version of online convex optimization where the lear...
We consider algorithms for "smoothed online convex optimization (SOCO)" problems. SOCO is a variant ...
We consider a natural online optimization problem set on the real line. The state of the online algo...
This paper presents competitive algorithms for a novel class of online optimization problems with me...
We study the performance of an online learner under a framework in which it receives partial informa...
We study online optimization in a setting where an online learner seeks to optimize a per-round hitt...
We consider algorithms for “smoothed online convex optimization” (SOCO) problems, which are a hybri...
We examine the problem of smoothed online optimization, where a decision maker must sequentially cho...
Online Convex Optimization (OCO) is a field in the intersection of game theory, optimization, and ma...
We consider algorithms for 'smoothed online convex optimization' (SOCO) problems, which are a hybrid...
We investigate the problem of distributed online convex optimization with complicated constraints, i...
Online optimization has emerged as powerful tool in large scale optimization. In this pa-per, we int...
We aim to design universal algorithms for online convex optimization, which can handle multiple comm...
Online learning and convex optimization algorithms have become essential tools for solving problems ...
We consider Online Convex Optimization (OCO) in the setting where the costs are mm-strongly convex a...
We study Smoothed Online Convex Optimization, a version of online convex optimization where the lear...
We consider algorithms for "smoothed online convex optimization (SOCO)" problems. SOCO is a variant ...
We consider a natural online optimization problem set on the real line. The state of the online algo...
This paper presents competitive algorithms for a novel class of online optimization problems with me...
We study the performance of an online learner under a framework in which it receives partial informa...
We study online optimization in a setting where an online learner seeks to optimize a per-round hitt...
We consider algorithms for “smoothed online convex optimization” (SOCO) problems, which are a hybri...
We examine the problem of smoothed online optimization, where a decision maker must sequentially cho...
Online Convex Optimization (OCO) is a field in the intersection of game theory, optimization, and ma...
We consider algorithms for 'smoothed online convex optimization' (SOCO) problems, which are a hybrid...
We investigate the problem of distributed online convex optimization with complicated constraints, i...
Online optimization has emerged as powerful tool in large scale optimization. In this pa-per, we int...
We aim to design universal algorithms for online convex optimization, which can handle multiple comm...
Online learning and convex optimization algorithms have become essential tools for solving problems ...