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Kramerovo pravilo je teorema u linearnoj algebri, koja daje izraz za rješenje sistema linearnih jednačina sa više nepoznatih, valid in those cases where there is unique solution; this solution is then expressed in terms of the determinants of the (square) coefficient matrix and of matrices obtained from it by replacing one column by the vector of right hand sides of the equations. It is named after Gabriel Cramer (1704–1752), who published the rule in his 1750 Introduction à l'analyse des lignes courbes algébriques, although Colin Maclaurin also published the method in his 1748 Treatise of Geometry (and probably knew of the method as early as 1729).
Computationally, it is inefficient for large matrices and thus not used in practical applications which may involve many equations. However, as no pivoting is needed, it is more efficient than Gaussian elimination for small matrices, particularly when SIMD operations are used.
Cramer's rule is of theoretical importance in that it gives an explicit expression for the solution of the system.
[uredi] Explicit formulas for small systems
Consider the linear system
which in matrix format is
Then, x and y can be found with Cramer's rule as:
The rules for 3×3 are similar. Given:
which in matrix format is
x, y and z can be found as follows:
[uredi] Finding solution of a system of linear equations in the general case
Consider a system of linear equations represented in matrix multiplication form as
Then the theorem states that:
where is the matrix formed by replacing the ith column of by the column vector .
This formula is, however, of limited practical value for larger matrices, as there are other more efficient ways of solving systems of linear equations, such as by Gauss elimination or, even better, LU decomposition.
[uredi] Finding inverse matrix
Let A be an n×n matrix. Then
where Adj(A) denotes the adjugate matrix of A, det(A) is the determinant, and I is the identity matrix. If det(A) is invertible in R, then the inverse matrix of A is
If R is a field (such as the field of real numbers), then this gives a formula for the inverse of A, provided det(A) ≠ 0. The formula is, however, of limited practical value for large matrices, as there are other more efficient ways of generating the inverse matrix, such as by Gauss–Jordan elimination.
[uredi] Abstract formulation
[uredi] Applications to differential geometry
Cramer's rule is also extremely useful for solving problems in differential geometry. Consider the two equations and . When u and v are independent variables, we can define and
Finding an equation for is a trivial application of Cramer's rule.
First, calculate the first derivatives of F, G, x, and y:
Substituting dx, dy into dF and dG, we have:
Since u, v are both independent, the coefficients of du, dv must be zero. So we can write out equations for the coefficients:
Now, by Cramer's rule, we see that:
This is now a formula in terms of two Jacobians:
Similar formulae can be derived for , ,
[uredi] Applications to algebra
[uredi] Relevance to eigenvalues
This can be viewed as a linear system of equations in which the coefficient matrix is the expression in the parentheses, the matrix of the unknowns is x, and the right hand side matrix is zero. According to Cramer's rule, solutions to x are expressed as a quotient. The numerator will always be zero, since here (see Eq. 1). Hence to find the non-trivial solutions (not all zeros), the denominator must also equal zero; that is, the determinant of the coefficient matrix must be zero. Then the solutions of the equation are given by:
which is the familiar characteristic equation
[uredi] Applications to integer programming
Cramer's rule can be used to prove that an integer programming problem whose constraint matrix is totally unimodular and whose right-hand side is all integer has integer basic solutions. This makes the integer program substantially easier to solve.
[uredi] See also
- Carl B. Boyer, A History of Mathematics, 2nd edition (Wiley, 1968), p. 431.