Optimization Online


A smooth perceptron algorithm

Negar Soheili (nsoheili***at***andrew.cmu.edu)
Javier Pena (jfp***at***andrew.cmu.edu)

Abstract: The perceptron algorithm, introduced in the late fifties in the machine learning community, is a simple greedy algorithm for finding a solution to a finite set of linear inequalities. The algorithm's main advantages are its simplicity and noise tolerance. The algorithm's main disadvantage is its slow convergence rate. We propose a modified version of the perceptron algorithm that retains the algorithm's original simplicity but has a substantially improved convergence rate.

Keywords: perceptron algorithm, smoothing technique, condition number

Category 1: Linear, Cone and Semidefinite Programming (Linear Programming )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation: Working Paper, Carnegie Mellon University, September 2011.

Download: [PDF]

Entry Submitted: 09/16/2011
Entry Accepted: 09/16/2011
Entry Last Modified: 09/22/2011

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society