Title: Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
Authors: Clauss, Philippe
Fernandez, Federico Javier
Garbervetsky, Diego
Verdoolaege, Sven
Issue Date: Oct-2006
Abstract: Memory requirement estimation is an important issue in the development
of embedded systems, since memory directly influences performance, cost
and power consumption. It is therefore crucial to have tools that
automatically compute accurate estimates of the memory
requirements of programs to better control the development process and avoid som
catastrophic execution exceptions.
Many important
memory issues can be expressed as the problem of maximizing a parametric polynom
defined over a parametric convex domain.
Bernstein expansion is a technique that has been used
to compute upper bounds on polynomials defined over intervals
and parametric ``boxes''.
In this paper, we propose an extension of this theory
to more general parametric convex domains and illustrate
its applicability to the resolution of memory issues with
several application examples.
Description: Technical Report 06-04, Université Louis Pasteur
Publication status: published
KU Leuven publication type: IR
Appears in Collections:Non-KU Leuven Association publications

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