When working with symbolic system of equations in Julia, one common task is to solve the equations for the unknown variables. In this article, we will explore three different ways to solve symbolic system of equations using the ax b method.

## Method 1: Using the `SymPy` package

The `SymPy` package in Julia provides a powerful set of tools for symbolic mathematics. To solve a symbolic system of equations using the ax b method, we can use the `linsolve` function from the `SymPy` package.

```
using SymPy
# Define the symbolic variables
@vars x y z
# Define the system of equations
eq1 = Eq(2*x + 3*y + 4*z, 10)
eq2 = Eq(3*x + 2*y + z, 5)
eq3 = Eq(x + y + 2*z, 8)
# Solve the system of equations
solution = linsolve([eq1, eq2, eq3], x, y, z)
```

The `linsolve` function takes the system of equations as input, followed by the variables to solve for. In this example, we solve for the variables `x`, `y`, and `z`. The solution is stored in the `solution` variable.

## Method 2: Using the `LinearAlgebra` package

The `LinearAlgebra` package in Julia provides functions for solving linear systems of equations. To solve a symbolic system of equations using the ax b method, we can use the `(backslash)` operator.

```
using LinearAlgebra
# Define the coefficient matrix
A = [2 3 4; 3 2 1; 1 1 2]
# Define the constant vector
b = [10; 5; 8]
# Solve the system of equations
solution = A b
```

In this example, we define the coefficient matrix `A` and the constant vector `b`. The `(backslash)` operator is used to solve the system of equations. The solution is stored in the `solution` variable.

## Method 3: Using the `NLSolve` package

The `NLSolve` package in Julia provides functions for solving nonlinear systems of equations. To solve a symbolic system of equations using the ax b method, we can use the `nlsolve` function.

```
using NLSolve
# Define the system of equations
function equations!(F, x)
F[1] = 2*x[1] + 3*x[2] + 4*x[3] - 10
F[2] = 3*x[1] + 2*x[2] + x[3] - 5
F[3] = x[1] + x[2] + 2*x[3] - 8
end
# Solve the system of equations
solution = nlsolve(equations!, [0.0, 0.0, 0.0])
```

In this example, we define the system of equations using a function `equations!`. The function takes an array `F` and an array `x` as input, and assigns the equations to the elements of `F`. The `nlsolve` function is used to solve the system of equations. The solution is stored in the `solution` variable.

After exploring these three methods, it is clear that using the `SymPy` package provides the most straightforward and concise way to solve symbolic system of equations using the ax b method. The `linsolve` function handles the symbolic equations directly, without the need to define coefficient matrices or write custom functions. Therefore, the first method using the `SymPy` package is the better option.