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yuenkf
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- sorry. erm.. what does the determinant means or functions in matrices , math? thanks..
There's a theorem that says that the following statements about an arbitrary ##n\times n##-matrix ##A## are equivalent:yuenkf said:why we want to get the determinant?
yuenkf said:why we want to get the determinant?
A determinant is a mathematical concept that is used to determine the properties of a square matrix. It is a single value that can be calculated from the elements of the matrix and is commonly denoted by |A| or det(A). It is used to solve systems of linear equations, find the inverse of a matrix, and determine whether a matrix is invertible or not.
The calculation of a determinant depends on the size of the matrix. For a 2x2 matrix, it is calculated by multiplying the elements in the first row and column and subtracting the product of the elements in the second row and column. For larger matrices, the process can become more complex, but there are various methods such as the cofactor expansion method or the Gaussian elimination method that can be used to calculate determinants.
Determinants have many applications in mathematics and science. They are used to solve systems of linear equations, find the area or volume of a parallelogram or parallelepiped, and determine whether a matrix is invertible or not. In physics, they are used to calculate the moment of inertia of objects and in computer graphics, they are used to transform and rotate objects in three-dimensional space.
Yes, a determinant can be negative. The sign of a determinant depends on the number of row or column swaps that are performed during its calculation. If an odd number of swaps are performed, the determinant will be negative, and if an even number of swaps are performed, the determinant will be positive.
Determinants and eigenvalues are closely related concepts. The determinant of a square matrix is equal to the product of all its eigenvalues. This relationship is used in many areas of mathematics, such as in the calculation of characteristic polynomials and the diagonalization of matrices.