• Block Relaxation Algorithms in Statistics -- Part II
  • Project
  • 1. Majorization Methods
    • 1.1. Introduction
    • 1.2. Definitions
      • 1.2.1. Majorization at a Point
      • 1.2.2. Majorization on a Set
      • 1.2.3. Majorization Algorithms
      • 1.2.4. Alternative Definitions
    • 1.3. Close Relatives
      • 1.3.1. Concave-Convex Procedure
      • 1.3.2. Generalized Weiszfeld Methods
      • 1.3.3. The EM Algorithm
      • 1.3.4. The Lower-Bound Principle
    • 1.4. Some Results
      • 1.4.1. Rate of Convergence
      • 1.4.2. Univariate and Separable Functions
      • 1.4.3. Differentiable Functions
      • 1.4.4. Composition
      • 1.4.5. Majorization Duality
      • 1.4.6. Necessary Conditions by Majorization
      • 1.4.7. Majorizing Constraints
      • 1.4.8. Majorizing Value Functions
    • 1.5. Some Examples
      • 1.5.1. The Reciprocal
      • 1.5.2. Cubics and Quartics
      • 1.5.3. Normal PDF and CDF
      • 1.5.4. Logistic PDF and CDF
  • 2. Inequalities for Majorization
    • 2.1. Introduction
    • 2.2. The AMGM Inequality
      • 2.2.1. Absolute Values
      • 2.2.2. Gini Mean Difference
      • 2.2.3. Location Problems
      • 2.2.4. The Lasso and the Bridge
    • 2.3. The Cauchy-Schwartz Inequality
      • 2.3.1. Rayleigh Quotient
      • 2.3.2. The Majorization Method for MDS
    • 2.4. Young's Inequality
      • 2.4.1. Support Vector Machines
  • 3. Using Convexity
    • 3.1. Introduction
    • 3.2. Jensen's Inequality
      • 3.2.1. Tomography
      • 3.2.2. Sums and Integrals
    • 3.3. The EM Algorithm
  • 4. Tangential Majorization
    • 4.1. Concave Functions
      • 4.1.1. Majorizing and Minorizing the Logarithm
      • 4.1.2. Aspects of Correlation Matrices
      • 4.1.3. Partially Observed Linear Systems
      • 4.1.4. Gpower
    • 4.2. Broadening the Scope
      • 4.2.1. Differences of Convex Functions
      • 4.2.2. Convexifiable Functions
      • 4.2.3. Piecewise Linear Majorization
  • 5. Quadratic Majorization
    • 5.1. Introduction
    • 5.2. Existence
    • 5.3. Convergence
    • 5.4. Bounding Second Derivatives
      • 5.4.1. Normal Density and Distribution
      • 5.4.2. Nondiagonal Weights in Least Squares
      • 5.4.3. Quadratic on a Sphere
      • 5.4.4. Gifi Goes Logistic
      • 5.4.5. A Matrix Example
      • 5.4.6. Gauss-Newton Majorization
      • 5.4.7. Margnal Functions
  • 6. Higher Order Majorization
    • 6.1. Introduction
    • 6.2. Mean Value Majorization
    • 6.3. Taylor Majorization
      • 6.3.1. Second Order
      • 6.3.2. Higher Order
    • 6.4. Nestorov Majorization
    • 6.5. Examples
      • 6.5.1. Revisiting the Reciprocal
      • 6.5.2. Logit
      • 6.5.3. Probit
  • 7. Sharp Majorization
    • 7.1. Introduction
    • 7.2. Comparing Majorizations
    • 7.3. Sharp Quadratic Majorization
      • 7.3.1. Existence
      • 7.3.2. Two-Point Majorization
      • 7.3.3. Even and Odd Functions
    • 7.4. Sharp Piecewise Linear
    • 7.5. Examples
      • 7.5.1. The Cosine
      • 7.5.2. The Rasch Model
      • 7.5.3. Logits
      • 7.5.4. Probits
  • 8. Local and Localized Majorization
    • 8.1. Introduction
    • 8.2. Majorization in the Neighborhood
      • 8.2.1. Cartesian Folium
      • 8.2.2. Univariate Cubics
      • 8.2.3. Majorization on a Sphere
      • 8.2.4. Majorization on a Hyperrectangle
    • 8.3. Proximal Point Majorization
    • 8.4. Sublevel Majorization
    • 8.5. Dinkelbach Majorization
  • 9. Notation
  • 10. Bibliography
  • 11. What's New
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Block Relaxation Algorithms in Statistics -- Part II

II.1.5: Some Simple Examples

  1. The Reciprocal
  2. Cubics and Quartics
  3. Normal PDF and CDF
  4. Logistic PDF and CDF