Symbolic polynomial maximization over convex sets and its application to memory requirement estimation

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 c...

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Autores principales:	Fernández, Federico Javier, Garbervetsky, Diego
Publicado:	2009
Materias:	Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10638210_v17_n8_p983_Clauss http://hdl.handle.net/20.500.12110/paper_10638210_v17_n8_p983_Clauss
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_10638210_v17_n8_p983_Clauss
record_format	dspace
spelling	paper:paper_10638210_v17_n8_p983_Clauss2023-06-08T16:04:05Z Symbolic polynomial maximization over convex sets and its application to memory requirement estimation Fernández, Federico Javier Garbervetsky, Diego Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization 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 some catastrophic execution exceptions. Many important memory issues can be expressed as the problem of maximizing a parametric polynomial 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. © 2006 IEEE. Fil:Fernández, F.J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Garbervetsky, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10638210_v17_n8_p983_Clauss http://hdl.handle.net/20.500.12110/paper_10638210_v17_n8_p983_Clauss
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization
spellingShingle	Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization Fernández, Federico Javier Garbervetsky, Diego Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
topic_facet	Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization
description	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 some catastrophic execution exceptions. Many important memory issues can be expressed as the problem of maximizing a parametric polynomial 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. © 2006 IEEE.
author	Fernández, Federico Javier Garbervetsky, Diego
author_facet	Fernández, Federico Javier Garbervetsky, Diego
author_sort	Fernández, Federico Javier
title	Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
title_short	Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
title_full	Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
title_fullStr	Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
title_full_unstemmed	Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
title_sort	symbolic polynomial maximization over convex sets and its application to memory requirement estimation
publishDate	2009
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10638210_v17_n8_p983_Clauss http://hdl.handle.net/20.500.12110/paper_10638210_v17_n8_p983_Clauss
work_keys_str_mv	AT fernandezfedericojavier symbolicpolynomialmaximizationoverconvexsetsanditsapplicationtomemoryrequirementestimation AT garbervetskydiego symbolicpolynomialmaximizationoverconvexsetsanditsapplicationtomemoryrequirementestimation
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Symbolic polynomial maximization over convex sets and its application to memory requirement estimation

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