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Ci-dessous, les différences entre deux révisions de la page.

--- teaching:methcalchim:system_of_linear_equations [2018/10/08 16:06] – villersd
+++ teaching:methcalchim:system_of_linear_equations [2018/10/18 10:10] (Version actuelle) – villersd
@@ Ligne 17: / Ligne 17: @@
   * Time complexity analysis
     * Hint : in Python, use the timeit module
+===== Jupyter notebooks =====
+  * Example file (to be continued) : [[https://notebooks.azure.com/linusable/libraries/samples-public/html/notebooks/calculation_methods_applied_to_chemistry/Gauss-Jordan-01.ipynb]]
 ===== Exercices and applications =====
@@ Ligne 29: / Ligne 32: @@
   * Using [[wp>Tridiagonal_matrix_algorithm|tridiagonal Thomas algorithm]] allows to save computational time thanks to n complexity
   * ? Python library with Thomas algorithm
+===== What you must have learned in this chapter =====
+  * Except ill-conditionned, linear systems can be solved "exactly" using linear algebra algorithms in a finite and known number of arithmetic operations.
+  * The accuracy is determined by the number of numerical figures which are encoded in floating point description
+  * For a general system of n equations, diagonalisation requires of the order of n<sup>3</sup> operations. Also for solving a system using these method.
+  * If the coefficient matrix is the same for different systems (only the independent coefficients are different), it is possible to solve systems with the order of n<sup>2</sup> operations, if the matrix of coeeficients is decomposed in the product of two triangular matrix (Lower-Upper decomposition). This n<sup>3</sup> step is realised only once.
 ===== References : =====