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In linear algebra, Cramer's rule is an explicit formula for the solution of a system of linear equations with as many equations as unknowns, valid whenever the system has a unique solution. It expresses the solution in terms of the determinants of the (square) coefficient matrix and of matrices obtained from it by replacing one column by the ...
Solving gives = , and substituting ... Cramer's rule is an explicit formula for the solution of a system of linear equations, with each variable given by a quotient ...
Cramer's rule is a closed-form expression, in terms of determinants, of the solution of a system of n linear equations in n unknowns. Cramer's rule is useful for reasoning about the solution, but, except for n = 2 or 3 , it is rarely used for computing a solution, since Gaussian elimination is a faster algorithm.
Determinants occur throughout mathematics. For example, a matrix is often used to represent the coefficients in a system of linear equations, and determinants can be used to solve these equations (Cramer's rule), although other methods of solution are computationally much more
For example, to solve a system of n equations for n unknowns by performing row operations on the matrix until it is in echelon form, and then solving for each unknown in reverse order, requires n(n + 1)/2 divisions, (2n 3 + 3n 2 − 5n)/6 multiplications, and (2n 3 + 3n 2 − 5n)/6 subtractions, [10] for a total of approximately 2n 3 /3 operations.
The total derivatives are found by totally differentiating the system of equations, dividing through by, say dr, treating dq / dr and dp / dr as the unknowns, setting dI = dw = 0, and solving the two totally differentiated equations simultaneously, typically by using Cramer's rule.
Musk’s comment about Cramer’s prediction being “alarming” plays into the “inverse Cramer” narrative. This stems back to the financial crisis of 2008, when the TV host’s stock picks ...
Determinants can be used to solve linear systems using Cramer's rule, where the division of the determinants of two related square matrices equates to the value of each of the system's variables. [12]