Discrete Piecewise Monotonic Approximation by a Strictly Convex Distance Function

Theory and algorithms are presented for the following smoothing problem. We are given n measurements of a real-valued function that have been altered by random errors caused by the deriving process. For a given integer k , some efficient algorithms are developed that approximate the data by minimizing the sum of strictly convex functions of the errors in such a way that the approximated values are made up of at most k monotonie sections. If k = 1, then the problem can be solved by a special strictly convex programming calculation. If k > 1, then there are 0(nk) possible choices of the monotonie sections, so that it is impossible to test each one separately. A characterization theorem is derived that allows dynamic programming to be used for dividing the data into optimal disjoint sections of adjacent data, where each section requires a single monotonie calculation. It is remarkable that the theorem reduces the work for a global minimum to 0(n) monotonie calculations to subranges of data and 0(ks2) computer operations, where s 2 is the number of sign changes in the sequence of the first divided differences of the data. Further, certain monotonicity properties of the extrema of best approximations with k and k 1, and with k and k 2 monotonie sections make the calculation quite efficient. A Fortran 77 program has been written and some numerical results illustrate the performance of the smoothing technique in a variety of data sets.