

Abdulrahim Ahmadov
Software Engineer
Exploring Systems of Linear Equations: General Solution, Pivot, and Free Variables
Solving systems of linear equations is a fundamental skill in mathematics. In this blog post, we explore the process of solving these systems using matrix operations, Gaussian elimination, and identifying free variables.
Introduction
Understanding and solving systems of linear equations is a fundamental skill in mathematics, particularly in linear algebra. In this blog post, we will delve into the process of solving these systems using matrix operations, Gaussian elimination, and identifying free variables. We’ll illustrate these concepts through an example.
Problem Statement
We begin with a system of linear equations:
Transforming the System
To solve the system, we apply row operations to transform it into row-echelon form, making it easier to identify relationships between the variables:
Solving the System
Parameter Constraint
The last equation tells us:
Thus, (a = -1) for the system to be consistent.
Back Substitution
Starting from the bottom and working upwards:
-
From x_4 - 2x_5 = 1:
Let x_5 = 0, then x_4 = 1. -
Substituting x_4 = 1 and x_5 = 0 into x_3 - x_4 + 3x_5 = -2:
x_3 = -1. -
Substituting x_3 = -1, x_4 = 1, and x_5 = 0 into x_1 - 2x_2 + x_3 - x_4 + x_5 = 0:
x_1 = 2 + 2x_2 + 2x_5.
Particular Solution
By setting x_2 = 0 and x_5 = 0, the particular solution becomes:
Contribution from Free Variables
The general solution includes contributions from free variables (x_2) and (x_5).
Contribution from (x_2):
Contribution from (x_5):
General Solution
Combining the particular solution with contributions from the free variables, the general solution is:
Conclusion
Solving systems of linear equations is a powerful tool in mathematics. By understanding matrix operations and identifying free variables, we can unlock multiple solutions and gain deeper insights into various problems. Whether dealing with theoretical mathematics or practical applications, mastering these techniques is invaluable.
Reference: “Mathematics for Machine Learning” by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong.