REVIEW 2 cited by
A Dynamical Systems Perspective on Nesterov Acceleration
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We present a dynamical system framework for understanding Nesterov's accelerated gradient method. In contrast to earlier work, our derivation does not rely on a vanishing step size argument. We show that Nesterov acceleration arises from discretizing an ordinary differential equation with a semi-implicit Euler integration scheme. We analyze both the underlying differential equation as well as the discretization to obtain insights into the phenomenon of acceleration. The analysis suggests that a curvature-dependent damping term lies at the heart of the phenomenon. We further establish connections between the discretized and the continuous-time dynamics.
Forward citations
Cited by 2 Pith papers
-
NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes
A finite-sampling neural architecture is dense in the Hilbert space of square-integrable predictable processes and attains best-N-term chaoslet rates for compressible or Malliavin-regular processes.
-
Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints
Continuous-time proximal gradient and Douglas-Rachford splitting flows are shown to be globally exponentially stable using integral quadratic constraints, with explicit rates.
Discussion (0). Continue with ORCID to comment.