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    <p>The short answer is that <code>G</code> is maintained by the optimizer as part of the minimization process, while the <code>(D_neg, D, and C)</code> arguments are passed in as-is from the <code>args</code> tuple.</p> <p>By default, <code>scipy.optimize.minimize</code> takes a function <code>fun(x)</code> that accepts one argument <code>x</code> (which might be an array or the like) and returns a scalar. <code>scipy.optimize.minimize</code> then finds an argument value <code>xp</code> such that <code>fun(xp)</code> is less than <code>fun(x)</code> for other values of <code>x</code>. The optimizer is responsible for creating values of <code>x</code> and passing them to <code>fun</code> for evaluation.</p> <p>But what if you happen to have a function <code>fun(x, y)</code> that has some additional parameter <code>y</code> that needs to be passed in separately (but is considered a constant for the purposes of the optimization)? This is what the <code>args</code> tuple is for. The <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize" rel="noreferrer">documentation</a> tries to explain how the args tuple is used, but it can be a little hard to parse:</p> <blockquote> <p>args: <em>tuple, optional</em></p> <p>Extra arguments passed to the objective function and its derivatives (Jacobian, Hessian).</p> </blockquote> <p>Effectively, <code>scipy.optimize.minimize</code> will pass whatever is in <code>args</code> as the remainder of the arguments to <code>fun</code>, using the asterisk arguments notation: the function is then called as <code>fun(x, *args)</code> during optimization. The <code>x</code> portion is passed in by the optimizer, and the <code>args</code> tuple is given as the remaining arguments.</p> <p>So, in your code, the value of the <code>G</code> element is maintained by the optimizer while evaluating possible values of <code>G</code>, and the <code>(D_neg, D, C)</code> tuple is passed in as-is.</p>
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    1. CONice explanation! Usually you would give a guess as to what you expect `G` to be, say `G0`. So instead of the form in the OP, you'd use `minimize(func, jac=jac_func, G0, args=(...,), ...)`. If you don't pass one, it usually starts at `G=1.` I think it's less confusing in this form, even if `G0=1.` as well.
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    2. COI agree, it'd be nice to be more explicit about the initial guess for `G0` -- in fact I'd never seen a call to `minimize` that omitted it.
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    3. CO@lmjohns3 @askewchan: the initial guess `x0` is not an optional parameter, as you can see in the documentation. The user must always explicitly specify it, and there is no default value.
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