# System of Simultaneous Linear Equations

A system of simultaneous linear equations can be represented and solved using matrices. This approach involves converting the system into matrix form, typically using matrix algebra and techniques like Gaussian elimination. Let's explore this:

### System of Simultaneous Linear Equations:

Consider a system of $m$ linear equations with $n$ variables:

$\begin{array}{rl}{a}_{11}{x}_{1}+{a}_{12}{x}_{2}+\cdots +{a}_{1n}{x}_{n}& ={b}_{1}\\ {a}_{21}{x}_{1}+{a}_{22}{x}_{2}+\cdots +{a}_{2n}{x}_{n}& ={b}_{2}\\ \\ {a}_{m1}{x}_{1}+{a}_{m2}{x}_{2}+\cdots +{a}_{mn}{x}_{n}& ={b}_{m}\end{array}$

This system can be represented in matrix form as $AX=B$, where:

• $A$ is the coefficient matrix of size $m×n$.
• $X$ is the column matrix of variables of size $n×1$.
• $B$ is the column matrix of constants on the right-hand side of equations of size $m×1$.

### Matrix Representation:

The system $AX=B$ can be represented as:

$\left[\begin{array}{cccc}{a}_{11}& {a}_{12}& \dots & {a}_{1n}\\ {a}_{21}& {a}_{22}& \dots & {a}_{2n}\\ & & \ddots & \\ {a}_{m1}& {a}_{m2}& \dots & {a}_{mn}\end{array}\right]×\left[\begin{array}{c}{x}_{1}\\ {x}_{2}\\ \\ {x}_{n}\end{array}\right]=\left[\begin{array}{c}{b}_{1}\\ {b}_{2}\\ \\ {b}_{m}\end{array}\right]$

### Solving Systems Using Matrices:

1. Matrix Operations: Perform elementary row operations on the augmented matrix $\left[A\mathrm{\mid }B\right]$ to bring it to row-echelon or reduced row-echelon form.

2. Matrix Inversion: If $A$ is a square matrix and invertible ($\text{det}\left(A\right)\mathrm{\ne }0$, the solution can be found using $X={A}^{-1}B$.

3. Gaussian Elimination: Use methods like Gaussian elimination or Gauss-Jordan elimination to solve the system by transforming the augmented matrix.

### Applications:

• Engineering and Physics: Used to solve systems of equations modeling various physical phenomena.

• Data Analysis: Systems of linear equations are fundamental in solving problems in data analysis and statistics.

### Homogeneous System of Linear Equations:

A homogeneous system of linear equations is one where all constant terms on the right-hand side of the equations are zero.

For instance, consider a system of equations:

$\begin{array}{rl}{a}_{11}{x}_{1}+{a}_{12}{x}_{2}+\cdots +{a}_{1n}{x}_{n}& =0\\ {a}_{21}{x}_{1}+{a}_{22}{x}_{2}+\cdots +{a}_{2n}{x}_{n}& =0\\ \\ {a}_{m1}{x}_{1}+{a}_{m2}{x}_{2}+\cdots +{a}_{mn}{x}_{n}& =0\end{array}$

The corresponding matrix equation $AX=0$ is represented as a homogeneous system, where $A$ is the coefficient matrix and $X$ is the column matrix of variables.

### Non-Homogeneous System of Linear Equations:

A non-homogeneous system of linear equations is one where there are non-zero constant terms on the right-hand side of at least one equation.

For example:

$\begin{array}{rl}{a}_{11}{x}_{1}+{a}_{12}{x}_{2}+\cdots +{a}_{1n}{x}_{n}& ={b}_{1}\\ {a}_{21}{x}_{1}+{a}_{22}{x}_{2}+\cdots +{a}_{2n}{x}_{n}& ={b}_{2}\\ \\ {a}_{m1}{x}_{1}+{a}_{m2}{x}_{2}+\cdots +{a}_{mn}{x}_{n}& ={b}_{m}\end{array}$

Here, if ${b}_{1},{b}_{2},\dots ,{b}_{m}$ are not all zero, it's a non-homogeneous system.

### Matrix Representation:

• For a homogeneous system, the matrix equation is $AX=0$.
• For a non-homogeneous system, the matrix equation is $AX=B$, where $B$ is a column matrix with non-zero values.

### Solutions:

• Homogeneous System: Always has at least one solution called the trivial solution (${x}_{1}={x}_{2}=\cdots ={x}_{n}=0$). It may also have non-trivial solutions when the determinant of the coefficient matrix is zero.

• Non-Homogeneous System: Can have either unique solutions, infinitely many solutions, or no solutions depending on the properties of the coefficient matrix $A$ and the constants in matrix $B$.

### Applications:

• Homogeneous Systems: Common in solving problems involving homogeneous physical systems and in finding the null space of a matrix.

• Non-Homogeneous Systems: Widely used in various real-world problems and applications in engineering, physics, economics, and other fields.

### Methods of Solution:

1. Matrix Inversion: If $A$ is square and invertible ($\text{det}\left(A\right)\mathrm{\ne }0$), the solution is $X={A}^{-1}B$.

2. Gaussian Elimination: Utilize Gaussian elimination or Gauss-Jordan elimination to transform the augmented matrix $\left[A\mathrm{\mid }B\right]$ to row-echelon or reduced row-echelon form. Solve for the variables from the resulting augmented matrix.

3. Matrix Factorization: Use techniques like LU decomposition or QR factorization to solve the system.

4. Inverse and Cramer's Rule: If $A$ is square and $\text{det}\left(A\right)\mathrm{\ne }0$, the solution can be found using $X={A}^{-1}B$. Cramer's rule can also be applied for smaller systems.

### Solutions:

• Unique Solution: If the system has a unique solution, it means there is one set of values for the variables that satisfy all equations.

• Infinite Solutions: In some cases, a system may have infinitely many solutions due to dependencies among equations.

• No Solution: If the system is inconsistent (contradictory equations), it has no solution.