A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is
This paper aims to improve the response speed of SPDC (stochastic primal–dual coordinate ascent) in large-scale machine learning, as the complexity of per-iteration of SPDC is not satisfactory. We propose an accelerated stochastic primal–dual coordinate ascent called ASPDC and its further accelerated variant, ASPDC-i. Our proposed ASPDC methods achieve a good balance between low per-iteration computation complexity and fast convergence speed, even when the condition number becomes very large.doi:10.3390/electronics11152382 fatcat:jnxrtaxo5rfrtn7kmosivr6msi