15 research outputs found
On complexity and convergence of high-order coordinate descent algorithms
Coordinate descent methods with high-order regularized models for
box-constrained minimization are introduced. High-order stationarity asymptotic
convergence and first-order stationarity worst-case evaluation complexity
bounds are established. The computer work that is necessary for obtaining
first-order -stationarity with respect to the variables of each
coordinate-descent block is whereas the computer
work for getting first-order -stationarity with respect to all the
variables simultaneously is . Numerical examples
involving multidimensional scaling problems are presented. The numerical
performance of the methods is enhanced by means of coordinate-descent
strategies for choosing initial points