# Avoid Blind Trust in Computer Algebra Systems for Solutions

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## Understanding Computer Algebra Systems

When utilizing computer algebra systems such as Mathematica, Maple, or SymPy, there's a strong temptation to rely on a single powerful command to tackle complex problems. However, exercising caution is crucial, as neglecting this can lead to significant errors. This is especially relevant when addressing differential equations. Let's begin a Jupyter Notebook session and examine a straightforward nonlinear first-order equation using SymPy:

While we could manually solve this (which we will do later), we could also leverage the dsolve function in SymPy:

This command provides an immediate solution to the differential equation. But is it truly correct? We will explore this further! The issue does not lie with SymPy; the same could occur in Mathematica or Maple. The onus is on the user to verify the accuracy of the solution. Let's investigate what could be amiss.

Before delving deeper, we need to address the integration constant C1 by applying an initial condition, for instance, y(0)=0:

We can select

to satisfy the initial condition. However, we haven’t defined the symbol C1 in SymPy; it was simply generated from the output of dsolve. How can we replace it with its actual value? One method is to utilize free_symbols:

This provides a set containing all free symbols in an expression. Free symbols are those not bound as summation indices. Sets can be cumbersome to work with. Although we can iterate over them, their lack of a defined order means we can't simply request the first or only element. We can convert it to a list, which in our case would contain just one value, and then access it using index notation [0]:

Now we can substitute the constant:

Alright, we now have our solution from SymPy. But is it accurate? Let’s compute the derivative of this equation:

The left side matches our original differential equation, so let's substitute:

Now, let’s check if this equation holds true for specific values of y:

That checks out.

Also correct.

Wait, that’s not right! It’s evident that our solution fails for certain values of y. How could this happen? According to our differential equation, the slope of the solution must always remain non-negative, since the square root is defined only for positive quantities. However, our "solution" resembles a sine function, which obviously exhibits both positive and negative slopes across different regions. Hence, our solution cannot be universally valid! But how do we pinpoint the error? The only way is to solve the equation incrementally instead of attempting to solve it all at once. Let’s proceed with that method.

## Step-by-Step Solution of Differential Equations

To start, we take our original differential equation and separate the variables, ensuring all terms involving y are on one side and those with x on the other (a method known as separation of variables):

Next, we integrate the equation:

and then solve for y:

This yields the same solution we derived from dsolve. So where did we go astray? We didn’t make an error, but we did operate under an implicit assumption. When we separated the variables, moving all terms involving y to one side and all terms involving x to the other, we had to divide by:

However, if y²=1 for any y, this results in division by zero! Clearly, we've overlooked certain solutions, specifically y=±1! In fact, the constant functions y=±1 do satisfy the differential equation, but they do not satisfy the initial condition y(0)=0. The only viable way to meet both the differential equation and the initial condition is to construct the solution piecewise for different intervals. One possible and continuous choice is:

In conclusion, when utilizing any computer algebra system to solve differential equations, always verify the solution. It may be incomplete! This reinforces the importance of mastering manual methods alongside modern computational tools.

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