DeepAI AI Chat
Log In Sign Up

Complexity Estimates for Fourier-Motzkin Elimination

11/05/2018
by   Rui-Juan Jing, et al.
0

In this paper, we propose a new method for removing all the redundant inequalities generated by Fourier-Motzkin Elimination. This method is based on Kohler's work and an improved version of Balas' work. Moreover, this method only uses arithmetic operations on matrices. Algebraic complexity estimates and experimental results show that our method outperforms alternative approaches based on linear programming.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/17/2019

A Simpler Approach to Linear Programming

Dantzig and Eaves claimed that fundamental duality theorems of linear pr...
07/17/2019

Solving Systems of Linear Inequalities

Dantzig and Eaves claimed that fundamental duality theorems of linear pr...
11/18/2019

An Efficient Parametric Linear Programming Solver and Application to Polyhedral Projection

Polyhedral projection is a main operation of the polyhedron abstract dom...
06/25/2018

Quantifier Elimination for Database Driven Verification

Running verification tasks in database driven systems requires solving q...
12/30/2019

Linear Programming using Limited-Precision Oracles

Since the elimination algorithm of Fourier and Motzkin, many different m...
02/23/2017

Algorithm for computing semi-Fourier sequences of expressions involving exponentiations and integrations

We provide an algorithm for computing semi-Fourier sequences for express...
05/27/2019

DBFFT: A displacement based FFT approach for homogenization of the mechanical behavior

All the FFT-based methods available for homogenization of the mechanical...