This paper considers online convex optimization with long term constraints, where
constraints can be violated in intermediate rounds, but need to be satisfied in the long run …

This paper considers online convex optimization over a complicated constraint set, which
typically consists of multiple functional constraints and a set constraint. The conventional …

We present an adaptive online gradient descent algorithm to solve online convex
optimization problems with long-term constraints, which are constraints that need to be …

We propose the algorithms for online convex optimization which lead to cumulative squared
constraint violations of the form $\sum\limits_ {t= 1}^ T\big ([g (x_t)] _+\big)^ 2= O (T^{1 …

In this paper we propose efficient algorithms for solving constrained online convex
optimization problems. Our motivation stems from the observation that most algorithms …

This paper considers online convex optimization with hard constraints and analyzes
achievable regret and cumulative hard constraint violation (violation for short). The problem …

Simultaneously Achieving Sublinear Regret and Constraint Violations for Online Convex
Optimization with Time-varying Constraints Page 1 Simultaneously Achieving Sublinear Regret …

We investigate online convex optimization in non-stationary environments and choose
dynamic regret as the performance measure, defined as the difference between cumulative …

We study the problem of safe online convex optimization, where the action at each time step
must satisfy a set of linear safety constraints. The goal is to select a sequence of actions to …

We introduce a new online convex optimization algorithm that adaptively chooses its
regularization function based on the loss functions observed so far. This is in contrast to …