734 research outputs found
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Many modern computer vision and machine learning applications rely on solving
difficult optimization problems that involve non-differentiable objective
functions and constraints. The alternating direction method of multipliers
(ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a
generalization of ADMM that often achieves better performance, but its
efficiency depends strongly on algorithm parameters that must be chosen by an
expert user. We propose an adaptive method that automatically tunes the key
algorithm parameters to achieve optimal performance without user oversight.
Inspired by recent work on adaptivity, the proposed adaptive relaxed ADMM
(ARADMM) is derived by assuming a Barzilai-Borwein style linear gradient. A
detailed convergence analysis of ARADMM is provided, and numerical results on
several applications demonstrate fast practical convergence.Comment: CVPR 201
Algorithms for Estimating Trends in Global Temperature Volatility
Trends in terrestrial temperature variability are perhaps more relevant for
species viability than trends in mean temperature. In this paper, we develop
methodology for estimating such trends using multi-resolution climate data from
polar orbiting weather satellites. We derive two novel algorithms for
computation that are tailored for dense, gridded observations over both space
and time. We evaluate our methods with a simulation that mimics these data's
features and on a large, publicly available, global temperature dataset with
the eventual goal of tracking trends in cloud reflectance temperature
variability.Comment: Published in AAAI-1
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