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2. Elimination theory - Wikipedia

   en.wikipedia.org/wiki/Elimination_theory

   This is the case of the theory of polynomials over an algebraically closed field, where elimination theory may be viewed as the theory of the methods to make quantifier elimination algorithmically effective. Quantifier elimination over the reals is another example, which is fundamental in computational algebraic geometry.

3. Cramer's rule - Wikipedia

   en.wikipedia.org/wiki/Cramer's_rule

   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 determinant.

4. System of polynomial equations - Wikipedia

   en.wikipedia.org/wiki/System_of_polynomial_equations

   Thus solving a polynomial system over a number field is reduced to solving another system over the rational numbers. For example, if a system contains 2 {\displaystyle {\sqrt {2}}} , a system over the rational numbers is obtained by adding the equation r 2 2 – 2 = 0 and replacing 2 {\displaystyle {\sqrt {2}}} by r 2 in the other equations.

5. Elementary algebra - Wikipedia

   en.wikipedia.org/wiki/Elementary_algebra

   Download as PDF; Printable version; ... An example of solving a system of linear equations is by using the elimination method:

6. Relaxation (iterative method) - Wikipedia

   en.wikipedia.org/wiki/Relaxation_(iterative_method)

   Relaxation methods were developed for solving large sparse linear systems, which arose as finite-difference discretizations of differential equations. [ 2 ] [ 3 ] They are also used for the solution of linear equations for linear least-squares problems [ 4 ] and also for systems of linear inequalities, such as those arising in linear programming .

7. LU decomposition - Wikipedia

   en.wikipedia.org/wiki/LU_decomposition

   The cost of solving a system of linear equations is approximately floating-point operations if the matrix has size . This makes it twice as fast as algorithms based on QR decomposition , which costs about 4 3 n 3 {\textstyle {\frac {4}{3}}n^{3}} floating-point operations when Householder reflections are used.