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Can I read Sharp Quadratic Majorization in One Dimension on EtoBox?
Sharp Quadratic Majorization in One Dimension by Jan de Leeuw; Kenneth Lange is a Mathematics article available to read on EtoBox.
What is Sharp Quadratic Majorization in One Dimension about?
Majorization methods solve minimization problems by replacing a complicated problem by a sequence of simpler problems. Solving the sequence of simple optimization problems guarantees convergence to a solution of the complicated original problem. Convergence is guaranteed by requiring that the approximating functions majorize the original function at the current solution. The leading examples of majorization are the EM algorithm and the SMACOF algorithm used in Multidimensional Scaling. The simplest possible majorizing subproblems are quadratic, because minimizing a quadratic is easy to do. In this paper quadratic majorizations for real-valued functions of a real variable are analyzed, and the concept of sharp majorization is introduced and studied. Applications to logit, probit, and robust loss functions are discussed.
Who reads Sharp Quadratic Majorization in One Dimension?
It is typically read by researchers, students, and practitioners in Mathematics.
- Author
- Jan de Leeuw; Kenneth Lange
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0167-9473)
- Published
- 2009
- Language
- EN
- Field
- Mathematics (Physical Sciences)