MACIS 2015 Session (SS6): Polynomial System Solving
Aim and Scope
Polynomial systems are fundamental objects that appear in numerous areas of science and engineering, such as, just to mention a few, Commutative Algebra, Algebraic Geometry, Geometric Reasoning, Algebraic Cryptanalysis, Signal Processing, and Biology. Consequently, algorithms for solving polynomial systems are very important and have various applications.
This special session is devoted to algorithms for solving polynomial systems and their implementations with applications. The main objective of this session is to bring together researchers interested in both theoretical and practical aspects of polynomial system solving to present and discuss recent results in this discipline.
Topics (including, but not limited to)
- Symbolic algorithms for solving polynomial systems
- Complexity analysis of algorithms for solving polynomial systems
- Efficient implementations of algorithms for solving polynomial systems
- Applications of polynomial system solving: Engineering, Cryptography, Coding Theory, Biology, etc.
- A short abstract will appear on the conference web page
as soon as accepted, and in the post-conference proceedings to be
published by LNCS.
- Several special issues of the journal Mathematics in Computer Science, published by Birkhauser/Springer, will be organized after the conference by session organizers. REGULAR (not SHORT) papers would be considered for these special issues.
- If you would like to give a talk at MACIS, you need to submit
at least a SHORT paper -- see
for the details. This session is designated as SS6.
- After the meeting,
the submission guideline for a journal special issue
will be communicated to you by the session organizers.
SHORT-SS6: Improving a CGS-QE algorithm
Ryoya Fukasaku, Hidenao Iwane, and Yosuke Sato
( Tokyo University of Science,  National Institute of Informatics / Fujitsu Laboratories Ltd)
A real quantifier elimination algorithm based on computation of comprehensive Groebner systems introduced by Weispfenning and recently improved by us has a weak point that it cannot handle a formula with many inequalities. In this paper, we further improve the algorithm so that we can handle more inequalities.