The Comparison Of Gaussian Elimination And Cholesky Composition Methods To Linear System Of Equation
This project work is concerned with study of the comparison of Gaussian elimination and cholesky decomposition methods to linear system of equations.
In chapter one, we are concerned with linear systems and the various methods of solving them. In chapter two and chapter three, we dealt with the indept study of Gaussian Elimination method and the cholesky Decomposition method, with their good points and bad points respectively. While chapter four deals with various application area of both methods, their comparison and finally conclusion..
Linear System of Equations
A wide variety of problems lead ultimately to the need to solve a linear system of equation linear system of equations are associated with many problems in engineering and science as well as with applications of mathematics to the social sciences and the quantitative study of business and economic problems.
In 1985, according to Atkinson, system of Simultaneous linear equation occur in solving problems in a wide variety of areas with respect to mathematics, statistics, physical quantities (examples are temperature, voltage, population management and displacement). Social sciences, engineering and business. They arise directly in solving real life problems.
The world sometimes reveals itself to us as observable relationships among the relevant variables what it does make evident are relationship that describe how both the variable and their rate of change affect each other.
Apparently, such life changing problem gives rise to systems of simultaneous linear equation. In almost every human activities, man seems to be compelled to uncover fundamental relationship that exist among the objects he observes. According to Maron in 1982, he said in order to make the relationship that exist between variables explicit, we frequently attempt to make a mathematical model that will accurately reflect real life situation. Many mathematical model that will accurately reflect real life situation. Many mathematical models have the same basic structure although disparity in Symbolic rotation may be utilized, which can arise from economics, transportation, which need may arise to make efficient allocation among several points or to solve the growth of population in which units of x1, x2 …., xn arises from net flow from one point to another or in relationship to population growth, that is, number of individuals in a particular age group at a particular time.
There are various methods in solving linear system of simultaneous equations. In numerical analysis the techniques and methods for solving system of linear equations belongs to two categories: Direct and Iterative methods. The direct methods obtain the exact solution (in real arithmetic) in finitely many operations where as iterative method generate a sequence of approximations that only converge in the limit to the solution. The direct method falls into two categories or clam that is the Gaussian elimination method and cholesky decomposition method. Some others are matrix inverse method and LU factorization method and the Cramer’s rule method.
The elimination approach reduces the given system of equations to a form from which the solution can be obtained by simple substitution since calculators and computers have some limit to the number of digits for their use this may lead to round-off errors and produces poorer results. Generally, the direct method are best for full or bounded matrices where as iterative methods are best for very large and sparse matrices. The iterative method provide an alternative to the direct methods for solving systems of linear equations. This method involves assumption of some initial values which are then refined repeatedly till they reach some accepter rang of accuracy. The Jacobi and Gawn-siedel methods are good examples of the iterative method.
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