Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Restart of accelerated first order methods with linear convergence under
  a quadratic functional growth condition   Accelerated first order methods, also called fast gradient methods, are
popular optimization methods in the field of convex optimization. However, they
are prone to suffer from oscillatory behaviour that slows their convergence
when medium to high accuracy is desired. In order to address this, restart
schemes have been proposed in the literature, which seek to improve the
practical convergence by suppressing the oscillatory behaviour. This paper
presents a restart scheme for accelerated first order methods for which we show
linear convergence under the satisfaction of a quadratic functional growth
condition, thus encompassing a broad class of non-necessarily strongly convex
optimization problems. Moreover, the worst-case convergence rate is comparable
to the one obtained using a (generally non-implementable) optimal fixed-rate
restart strategy. We compare the proposed algorithm with other restart schemes
by applying them to a model predictive control case study.

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The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
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The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg