Gaussian Elimination: Solving Systems of Linear Equations

In summary, Gaussian elimination with pivoting is commonly used in computer software to solve systems of linear equations. However, there are other methods such as conjugate gradients and matrix decomposition that may be more effective in certain cases. LU decomposition is one type of matrix decomposition used for solving linear equations, but other methods such as Cholesky, LDLT, QR, and SVD may also be utilized.
  • #1
matqkks
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Is it correct that the Gaussian elimination procedure is used in computer software to solve systems of linear equations?
 
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  • #2
http://www.mathworks.com/access/helpdesk/help/techdoc/math/f4-983672.html
 
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In short: yes (with pivoting for numerical stability), but it's far from the only way, and in certain cases other methods may be better (for example, some method based on conjugate gradients is good when the matrix is sparse, and matrix decomposition is useful for when you want to solve Ax = b for several different b).
 
  • #4
There are a variety of matrix decompositions that are used in solving sets of linear equations. LU decomposition is based on Gauss elimination:

http://en.wikipedia.org/wiki/LU_decomposition

but other methods (e.g. Cholesky, LDLT, QR, SVD, etc.) may not.
 
  • #5


Yes, it is correct that the Gaussian elimination procedure is commonly used in computer software to solve systems of linear equations. This method, also known as row reduction, is a systematic process of manipulating a matrix to transform it into its reduced row echelon form. This allows for the efficient and accurate solution of a system of equations, which is important in many fields of science and engineering. The algorithm is also relatively easy to implement in computer code, making it a popular choice for solving linear equations in software applications.
 

Related to Gaussian Elimination: Solving Systems of Linear Equations

What is Gaussian Elimination and how does it work?

Gaussian Elimination is a systematic method for solving systems of linear equations. It involves using elementary row operations to transform the system of equations into an equivalent system with a triangular matrix, making it easier to solve. The method works by eliminating one variable at a time until only one variable remains, which can then be solved for.

What are the elementary row operations used in Gaussian Elimination?

The three elementary row operations used in Gaussian Elimination are: (1) Interchanging two rows, (2) Multiplying a row by a non-zero constant, and (3) Adding a multiple of one row to another row. These operations can be used to manipulate the coefficients and constants in the equations without changing the solution set.

Can Gaussian Elimination be used to solve any system of linear equations?

Yes, Gaussian Elimination can be used to solve any system of linear equations. However, the method may not always be the most efficient or practical for larger systems. In some cases, other methods such as matrix inversion or Cramer's rule may be more suitable.

What is the difference between forward and backward elimination in Gaussian Elimination?

In forward elimination, the system of equations is transformed into an upper triangular matrix by eliminating variables from the bottom rows first. In backward elimination, the system is transformed into a lower triangular matrix by eliminating variables from the top rows first. Both methods result in the same solution, but the order in which the variables are eliminated is reversed.

Are there any limitations or drawbacks to using Gaussian Elimination?

One limitation of Gaussian Elimination is that it may become numerically unstable when dealing with large or ill-conditioned systems of equations. This means that small rounding errors in the calculations can quickly accumulate and lead to inaccurate solutions. In these cases, alternative methods such as iterative methods or LU decomposition may be more reliable.

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