We consider the piecewise linear approximation of saddle functions of the form (f(x,y)=ax^2-by^2 ) under the (L_ ) error norm. We show that interpolating approximations are not optimal. One can get slightly smaller errors by allowing the vertices of the approximation to move away from the graph of the function.
In arbitrary dimensions, the problem of optimal piecewise linear approximation has been adressed by Clarkson @cite_4 , without deriving explicit constant factors. For convex functions and convex bodies, there is a vast literature on optimal piecewise linear approximation in many variations, see for example the treatment in @cite_3 and the references given there.
@cite_4 @cite_3
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We study piecewise linear approximation of quadratic functions de- flned on R n . Invariance properties and canonical Caley Klein metrics that help in understanding this problem can be handled in arbitrary dimensions. However, the problem of optimal approximants in the sense that their linear pieces are of maximal size by keeping a given error tolerance, is a di-cult one. We present a detailled discussion of the case n = 2, where we can partially use results from convex geometry and discrete geometry. The case n = 3 is considerably harder, and thus just a few results can be formulated so far. We speed up previous (1 + e)-factor approximation algorithms for a number of geometric optimization problems in fixed dimensions: diameter, width, minimum-radius enclosing cylinder, minimum-width enclosing annulus, minimum-width enclosing cylindrical shell, etc. Linear time bounds were known before; we further improve the dependence of the "constants" in terms of e.We next consider the data-stream model and present new (1 + e)-factor approximation algorithms that need only constant space for all of the above problems in any fixed dimension. Previously, such a result was known only for diameter.Both sets of results are obtained using the core-set framework recently proposed by Agarwal, Har-Peled, and Varadarajan. Published by Elsevier B.V. In this paper we demonstrate a general method of designing constant-factor approximation algorithms for some discrete optimization problems with cardinality constraints. The core of the method is a simple deterministic ("pipage") procedure of rounding of linear relaxations. By using the method we design a (1-(1-1 k)k)-approximation algorithm for the maximum coverage problem where k is the maximum size of the subsets that are covered, and a 1 2-approximation algorithm for the maximum cut problem with given sizes of parts in the vertex set bipartition. The performance guarantee of the former improves on that of the well-known (1 - e-1)-greedy algorithm due to Cornuejols, Fisher and Nemhauser in each case of bounded k. The latter is, to the best of our knowledge, the first constant-factor algorithm for that version of the maximum cut problem. A new piecewise linear method is presented for the approximation of digitized curves. This method produces a sequence of consecutive line segments and has the following characteristics: (i) it approximates the digitized curve with the minimum number of line segments, (ii) the Euclidean distance between each point of the digitized curve and the line segment that approximates it, does not exceed a boundary value spl epsiv and (iii) the vertices of the produced line are not (necessarily) points of the input curve.