Abstract
This paper proposes the python coding for ST decomposition for Triangular, Trapezoidal, and computing the algorithms for the fully fuzzy linear system in python programming. where is a fuzzy matrix, are fuzzy vectors. ST decompose into a product of symmetric matrix (S) and triangular matrix (T) in the form of triangular and trapezoidal fuzzy number matrices. To best illustrate the proposed methods by python coding algorithm with a new approach Python coding has been adopted. Algorithms have been introduced and the numerical examples have been solved by using python techniques. A study of ST decomposition have been done and the solution is obtained with different algorithms. New numerical problems are presented and an example has been solved for this algorithms and the solutions are obtained.
Highlights
INTRODUCTIONSohrabi[8] initiated Cholesky Decomposition for solving the fully fuzzy linear system of equations in International Journal of Applied Mathematics
Fuzzy sets with different types are defined to identify the vagueness of the existing problems
Imprecise values are denoted as fuzzy number
Summary
Sohrabi[8] initiated Cholesky Decomposition for solving the fully fuzzy linear system of equations in International Journal of Applied Mathematics. Vijayalakshmi V.et al [10,11,12] introduced the concepts of solving FFLS for triangular, trapezoidal, hexagonal and octagonal fuzzy numbers. Venkatesan, A[9] introduced algorithm for a fully fuzzy linear system with Hexagonal fuzzy number matrix by singular value decomposition, International Journal of Computing Algorithms. Interactive performance is further enhanced by Python’s concise, clear syntax, it’s very high-level data types and its lack of declarations (which is compensated by run-time of the type checking) All this makes programming in Python feel like a leisure trip compared to the hard work involved in writing and debugging even a smallish C program.
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