Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Acceleration in First Order Quasi-strongly Convex Optimization by ODE
  Discretization   We study gradient-based optimization methods obtained by direct Runge-Kutta
discretization of the ordinary differential equation (ODE) describing the
movement of a heavy-ball under constant friction coefficient. When the function
is high order smooth and strongly convex, we show that directly simulating the
ODE with known numerical integrators achieve acceleration in a nontrivial
neighborhood of the optimal solution. In particular, the neighborhood can grow
larger as the condition number of the function increases. Furthermore, our
results also hold for nonconvex but quasi-strongly convex objectives. We
provide numerical experiments that verify the theoretical rates predicted by
our results.

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The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat