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Found 5 papers, 0 papers with code
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning.
Proximal operators for multi-agent path planning
We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms.
A message-passing algorithm for multi-agent trajectory planning
We describe a novel approach for computing collision-free \emph{global} trajectories for $p$ agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM).
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing
In this context, we focus on the Three-Weight Algorithm, which aims to solve general optimization problems.
An Improved Three-Weight Message-Passing Algorithm
We describe how the powerful "Divide and Concur" algorithm for constraint satisfaction can be derived as a special case of a message-passing version of the Alternating Direction Method of Multipliers (ADMM) algorithm for convex optimization, and introduce an improved message-passing algorithm based on ADMM/DC by introducing three distinct weights for messages, with "certain" and "no opinion" weights, as well as the standard weight used in ADMM/DC.
