• Block Relaxation Algorithms in Statistics -- Part I
  • Project
  • 1. Preface
  • 2. Introduction
    • 2.1. Some History
    • 2.2. Optimization Methods
  • 3. Block Relaxation
    • 3.1. Introduction
    • 3.2. Definition
    • 3.3. First Examples
      • 3.3.1. Two-block Least Squares
      • 3.3.2. Multiple-block Least Squares
    • 3.4. Generalized Block Relaxation
      • 3.4.1. Rasch Model
      • 3.4.2. Nonlinear Least Squares
    • 3.5. Block Order
      • 3.5.1. Projecting Blocks
    • 3.6. Rate of Convergence
      • 3.6.1. LU-form
      • 3.6.2. Product Form
      • 3.6.3. Block Optimization Methods
      • 3.6.4. Block Newton Methods
      • 3.6.5. Constrained Problems
    • 3.7. Additional Examples
      • 3.7.1. Canonical Correlation
      • 3.7.2. Singular Value Decomposition
      • 3.7.3. Optimal Scaling with LINEALS
      • 3.7.4. Multinormal Maximum Likelihood
      • 3.7.5. Array Multinormals
      • 3.7.6. Rasch, Once More
    • 3.8. Counterexamples
      • 3.8.1. Convergence to a Saddle
      • 3.8.2. Convergence to Incorrect Solution
      • 3.8.3. Nonconvergence and Cycling
      • 3.8.4. Sublinear Convergence
  • 4. Coordinate Descent
    • 4.1. Introduction
    • 4.2. Rate of Convergence
    • 4.3. Examples
      • 4.3.1. The Cartesian Folium
      • 4.3.2. A Family of Quadratics
      • 4.3.3. Loglinear Models
      • 4.3.4. Rayleigh Quotient
      • 4.3.5. Squared Distance Scaling
      • 4.3.6. Least Squares Factor Analysis
  • 5. Alternating Least Squares
    • 5.1. Introduction
    • 5.2. Close Relatives
      • 5.2.1. ALSOS
      • 5.2.2. ACE
      • 5.2.3. NIPALS and PLS
    • 5.3. Rate of Convergence
    • 5.4. Examples
      • 5.4.1. Homogeneity Analysis
      • 5.4.2. Bilinear Fitting and Fixed Rank Approximation
      • 5.4.3. Multilinear Fitting
      • 5.4.4. MCR-ALS
      • 5.4.5. Scaling and Splitting
  • 6. Augmentation and Decomposition Methods
    • 6.1. Introduction
    • 6.2. Definition
    • 6.3. Rate of Convergence
    • 6.4. Half-Quadratic Methods
    • 6.5. Examples
      • 6.5.1. Yates Augmentation
      • 6.5.2. Optimal Scaling with ORDINALS
      • 6.5.3. Least Squares Factor Analysis
      • 6.5.4. Squared Distance Scaling
      • 6.5.5. Linear Mixed Model
    • 6.6. Decomposition Methods
      • 6.6.1. Quadratic Form on a Sphere
      • 6.6.2. Multidimensional Unfolding
  • 7. Notation
  • 8. Bibliography
  • 9. What's New
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Block Relaxation Algorithms in Statistics -- Part I

I.5.4 Examples

  1. Homogeneity Analysis
  2. Bilinear Fitting and Fixed Rank Approximation
  3. Multilinear Fitting
  4. MCR-ALS
  5. Sacling and Splitting