Abstract
Consider the irrigation of an arbitrary measure on a domain X starting from a single source \( \mu + = \delta _0 \). In this chapter we define following Santambrogio [75] a function z(x) that represents the elevation of the landscape associated with an optimal traffic plan. This function was known in the geophysical community under a very strong discretization [6], [73]. The continuous landscape function will be proven to share all the properties that hold in the discrete case, in particular the fact that, at a point x0 of the irrigation network, z(x) has maximal slope in the direction of the network itself and that this slope is given by a power of the multiplicity, \( \left| x \right|_P^{\alpha - 1} \). Section 11.1 describes the physical discrete model of joint landscape-river network evolution. In Section 11.2 we will prove a general first variation inequality for the energy Eα when \( \mu ^ + \) varies. Section 11.3 proves that the definition of z(x), which will be defined as a path integral along the network from the source to x. does not depend on the path but only on x. Section 11.4 is devoted to a general semicontinuity property of z(x) and Section 11.5 to its Hölder continuity when the irrigated measure dominates Lebesgue on X. The whole chapter follows closely the seminal Santamabrogio paper [75].
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(2009). The Landscape of an Optimal Pattern. In: Optimal Transportation Networks. Lecture Notes in Mathematics, vol 1955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69315-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-69315-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69314-7
Online ISBN: 978-3-540-69315-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)