Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Optimization in Dynamic Environments: Improved Regret Rates for
  Strongly Convex Problems   In this paper, we address tracking of a time-varying parameter with unknown
dynamics. We formalize the problem as an instance of online optimization in a
dynamic setting. Using online gradient descent, we propose a method that
sequentially predicts the value of the parameter and in turn suffers a loss.
The objective is to minimize the accumulation of losses over the time horizon,
a notion that is termed dynamic regret. While existing methods focus on convex
loss functions, we consider strongly convex functions so as to provide better
guarantees of performance. We derive a regret bound that captures the
path-length of the time-varying parameter, defined in terms of the distance
between its consecutive values. In other words, the bound represents the
natural connection of tracking quality to the rate of change of the parameter.
We provide numerical experiments to complement our theoretical findings.

18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex