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Solving an equation symbolically means that expressions can be used for representing the solutions. For example, the equation x + y = 2x – 1 is solved for the unknown x by the expression x = y + 1, because substituting y + 1 for x in the equation results in (y + 1) + y = 2(y + 1) – 1, a true statement.
Solving the system of 6 unknowns formed by the last 6 equations result in the values of the polynomial coefficients: a 0 = 0 , a 1 = 0 , a 2 = 0 , a 3 = 10 , a 4 = − 15 , a 5 = 6. {\displaystyle a_{0}=0,\quad a_{1}=0,\quad a_{2}=0,\quad a_{3}=10,\quad a_{4}=-15,\quad a_{5}=6.}
[4] [5] [6] Cramer's rule, implemented in a naive way, is computationally inefficient for systems of more than two or three equations. [7] In the case of n equations in n unknowns, it requires computation of n + 1 determinants, while Gaussian elimination produces the result with the same computational complexity as the computation of a single ...
Substitute this expression into the remaining equations. This yields a system of equations with one fewer equation and unknown. Repeat steps 1 and 2 until the system is reduced to a single linear equation. Solve this equation, and then back-substitute until the entire solution is found. For example, consider the following system:
We have the following possible cases for an overdetermined system with N unknowns and M equations (M>N). M = N+1 and all M equations are linearly independent. This case yields no solution. Example: x = 1, x = 2. M > N but only K equations (K < M and K ≤ N+1) are linearly independent. There exist three possible sub-cases of this:
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.
For a system of linear equations, the number of equations in an indeterminate system could be the same as the number of unknowns, less than the number of unknowns (an underdetermined system), or greater than the number of unknowns (an overdetermined system). Conversely, any of those three cases may or may not be indeterminate.
It is inconsistent if and only if 0 = 1 is a linear combination (with polynomial coefficients) of the equations (this is Hilbert's Nullstellensatz). If an underdetermined system of t equations in n variables ( t < n ) has solutions, then the set of all complex solutions is an algebraic set of dimension at least n - t .