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Many thanks @Zhengyizhe0209-arch! It's a nice PR.

I think the wording should be a bit more specific because the lecture calls linprog without setting method, so SciPy uses the default method='highs'.

In SciPy, highs is an interface to the HiGHS optimization solver, and it automatically chooses between the HiGHS dual simplex solver and the HiGHS interior-point solver.

Because of this, I would avoid saying that SciPy necessarily converts the problem to standard form by adding slack variables before solving, since that description is mainly tied to the legacy linprog methods.

I think this is also the point that is being raised in #599.

A simpler replacement could be:

SciPy accepts inequality constraints in the form $A_{ub} x \leq b_{ub}$, equality constraints in the form $A_{eq} x = b_{eq}$, and variable bounds.

In this lecture, linprog uses SciPy’s default highs method, which calls the HiGHS optimization solver.

The slack value returned by linprog is a one-dimensional NumPy array whose entries measure the difference $b_{ub} - A_{ub}x$ for each inequality constraint.

Another small comment: you noted that this PR addresses #599, but it only tackles the first part of the issue.

It would be nice to address the entire issue in one PR or explicitly state that it addresses the first or second part of the issue to avoid closing it without fully resolving it.