## Recently Published

##### Weighted Low-rank Approximation using Majorization

We give a majorization algorithm for weighted low-rank matrix approximation, a.k.a. principal component analysis. There is one non-negative weight for each residual. A quadratic programming method is used to compute optimal rank-one weights for the majorization scheme.

##### Some Majorization Theory for Weighted Least Squares

In many situations in numerical analysis least squares loss functions with
diagonal weight matrices are much easier to minimize than least square loss functions with full positive semi-definite weight matrices. We use majorization to replace problems with a full weight matrix by a sequence of diagonal weight matrix problems. Diagonal weights which optimally approximate the full weights are computed using a simple semi-definite programming procedure.

##### Simultaneous Diagonalization of Positive Semi-definite Matrices

We give necessary and sufficient conditions for solvability of Aj=XWjX′ , with the Aj are m given positive semi-definite matrices of order n. The solution X
is n×p and the m solutions Wj are required to be diagonal, positive semi-definite, and adding up to the identity. We do not require that p≤n.

##### Infeasible Primal-Dual Quadratic Programming with Box Constraints

This describes a C version of a infeasible primal-dual algorithm for positive
definite quadratic programming with box constraints, proposed by Voglis and Lagaris. We also describe a .C() interface for R.

##### Factor Analysis as Matrix Decomposition and Approximation: I

A general form of linear factor analysis is defined, and presented as a method to factor a data matrix, similar in many respects to principal component analysis. We discuss necessary and sufficient conditions for solvability of the factor analysis equations and give a constructive method to compute all solutions. A follow up paper will present the corresponding algorithm.

##### Computing and Fitting Monotone Splines

A brief introduction to spline functions and B-splines, and specifically to monotone spline functions – with code in R and C and with some applications.

##### Exceedingly Simple Monotone Regression (with Ties)

A C implementation of Kruskal’s up-and-down-blocks monotone regression algorithm for use with .C() is extended to include the three classic ways of handling ties. It is then compared with other implementations.

##### Exceedingly Simple Monotone Regression

A C implementation of Kruskal’s up-and-down-blocks monotone regressionalgorithm for use wth .C(), and a comparison with other implementations.

##### Exceedingly Simple Sorting with Indices

We use the system qsort to write a routine that produces both the sort an the order of a vector of doubles.

##### Shepard Non-metric Multidimensional Scaling

We give an algorithm, with R code, to minimize the multidimensional scaling loss function proposed in Shepard’s 1962 papers. We show the loss function can be justified by using the classical rearrangement inequality, and we investigated its differentiability.

##### Multidimensional Scaling with Distance Bounds

We give an algorithm, with R code, to minimize the multidimensional scaling stress loss function under the condition that some or all of the fitted distances are between given positive upper and lower bounds. This paper combines theory, algorithms, code, and results of De Leeuw (2017b) and De Leeuw (2017a).

##### Multidimensional Scaling with Lower Bounds

We give an algorithm, with R code, to minimize the multidimensional scaling stress loss function under the condition that some or all of the fitted distances are larger than given positive lower bounds. Some interesting majorization theory is also given.

##### Multidimensional Scaling from Below

We give an algorithm, with R code, to minimize the multidimensional scaling stress loss function under the condition that all fitted distances are smaller than or equal to the corresponding dissimilarities.

##### Quadratic Programming with Quadratic Constraints

We give a quick and dirty, but reasonably safe, algorithm for the minimization of a convex quadratic function under convex quadratic constraints. The algorithm minimizes the Lagrangian dual by using a safeguarded Newton method with non-negativity constraints.

##### Multidimensional Scaling with Anarchic Distances

Using anarchic distances means using a different configuration for each dissimilarity. We give the anarchic version of the smacof majorization algorithm, and apply it to additive constants, individual differences, and scaling of asymmetric dissimilarities.

##### Least Squares Solutions of Linear Inequality Systems

We discuss the problem of finding an approximate solution to an overdetermined system of linear inequalities, or an exact solution if the system is consistent. Theory and R code is provided for four different algorithms. Two techniques use active set methods for non-negatively constrained least squares, one uses alternating least squares, and one uses a nonsmooth Newton method.

##### Discrete Minimax by Quadratic Majorization

We construct piecewise quadratic majorizers for minimax problems. This is appled to finding roots of cubics. An application to a Chebyshev versions of MDS loss is also outlined.

##### Majorizing Cubics on Intervals

We illustrate uniform quadratic majorization, sharp quadratic majorization, and sublevel quadratic majorization using the example of a univariate cubic.

##### Derivatives of Low Rank PSD Approximation

In De Leeuw (2008) we studied the derivatives of the least squares rank p
approximation in the case of general rectangular matrices. We modify these results for the symmetric positive semi-definite case, using basically the same derivation. We apply the formulas to compute the convergence rate of Thomson’s iterative principal component algorithm for factor analysis.

##### Convergence Rate of ELEGANT Algorithms

We compute the convergence rate of the ELEGANT algorithm for squared distance scaling by using an analytical expression for the derivative of the algorithmic map.

##### Zangwill/Ostrowski Descent Algorithms

This note collects the general results I have used over the years to prove and study convergence of alternating least squares, augmentation, and majorization algorithms. It does not aim for maximum generality or precision.

##### An Alternating Least Squares Approach to Squared Distance Scaling

Alternating Least Squares and Majorization approaches to squared distance scaling are discussed, starting from a "lost paper" from 1975.

##### Block Relaxation as Majorization

We show all block relaxation problems can be reformulated as majorization problems.

##### Pictures of Stress

A low-dimensional multidimensional scaling example is used to illustrate properties of the stress loss function and of different iteration methods

##### Gower Rank

In Multidimensional Scaling we sometimes find that stress does not decrease if we increase dimensionality. This is explained in this note by using the Gower rank. Two examples with small Gower rank are analyzed.

##### Exceedingly Simple Principal Pivot Transforms

Principal pivoting transforms are described and implemented using R routines with .C() wrappers.

##### RPubs by Jan

List of RPubs in 2016

##### Minimizing qStress for small q

We derive a majorization algorithm for the multidimensional scaling loss
function qStress, with q small.

##### APL in R

We provide the main functions of the APL array language that do not have R equivalents, using .C() and .Call() for the C interfaces.

##### In Praise of QR

R and C code for linear least squares, solving linear equations, computing null spaces, and computing Moore-Penrose inverses.

##### Exceedingly Simple Permutations and Combinations

Generate the next permutation or combination in lexicographic order.

##### Singularities and Zero Distances in Multidimensional Scaling

We analyze the stationary equations for Euclidean MDS when there are row-singularities, in the form of zero distances, and column singularities , the form of linear dependencies.

##### More on Inverse Multidimensional Scaling

For a given configuration we find the dissimilarity matrices for which the configuration is a stationary point of the corresponding least squares Euclidean multidimensional scaling problem.

##### Full-dimensional Scaling

We discuss least squares multidimensional scaling in high-dimensional space, where the problem becomes convex and has a unique solution.

##### Exceedingly Simple Isotone Regression with Ties

The primary, secondary, and tertiary approach to ties in monotone regressions are implemented in R, on top of the AS 149 Fortran algorithm of Cran.

##### Second Derivatives of rStress, with Applications

Derivatives of the rStress loss function for MDS are used to derive sensitivity regions and Newton-type algorithms.

##### Differentiability of rStress at a Local Minimum

The MDS loss function rStress is differentiable, directionally differentiable, or not differentiable, depending on the value of the power r.

##### Minimizing rStress using Majorization

Majorization is used to construct a class of algorithms that minimize least squares MDS loss functions such as stress used by Kruskal, stress used by Takane et el, and the loss used by Ramsay. More generally, majorization allows us to fit any positive power of Euclidean distances to a matrix of dissimilarities.

##### Croissants and Wedges in Multiple Correspondence Analysis

This paper discusses the horseshoe or Guttman effect in MCA and reviews various ways of handling curved scatterplots.

##### Multiset Canonical Correlation Analysis

Canonical correlations for more than two sets of variables, connections with multiple correspondence analysis, an alternative canonical form, and a permutation test.

##### Homogeneity Analysis

R Code and manual for a general version of homogeneity analysis that uses B-spline bases instead of binary indicators.

##### Exceedingly Simple Gram-Schmidt Code

Gram-Schmidt (without safeguards and pivots) in C and R.

##### Aspects of Correlation Matrices

Maximize nonlinear convex functions of the correlation matrix, such as eigenvalues, determinants, and multiple correlations, over monotone and non-monotone spline transformations of the variables.

##### Matrix Multiplication Animation

For 202BW14

##### Indexing a Matrix

For 202BW14

##### Gaussian Elimination

For 202BW14

##### LDU decomposition and pivots

For 202BW14

##### Bases, Orthonormal matrices, and Projectors

For 202BW14

##### Sweeping and the Partial Inverse

For 202BW14

##### Exceedingly Simple B-spline Code

R and C code to compute B-spline basis functions.

##### Regression with Linear Inequality Restrictions on Predicted Values

Simple fitting of monotone, non-negative, convex splines and polynomials.

##### Power Method Animation

For Statistics 102A, Fall 2013

##### Correlation Ellipses and Eigenvalues

For Statistics 102A, Fall 2013

##### A Profiling Example

For Statistics 102A, Fall 2013

##### Fixed Point Iterations

For Statistics 102A, Fall 2013

##### Speeding Up by Compiling

For Statistics 102A, Fall 2013

##### Connections

For Statistics 102A, Fall 2013

##### CLT Animation

For Statistics 102A, Fall 2013
Requires R2SWF and Flash

##### Low Order B-Splines

For Statistics 102A, Fall 2013
Requires R2SWF and Flash

##### Two-Sample Two-Sided Permutation Testing

For Statistics 102A, Fall 2013

##### Functions with a variable number of arguments

For Statistics 102A, Fall 2013