Using dynamic programming,
Here's the outline of my solution 1. How is it? Let \(y_2=1-x_2\). Also let's change the other x's to y's. Then we have
We can deduce that \(y_2 \ge 0\), but do we need to? In this case I guess not, but in general? If we solve this problem by dynamic programming, then we get the same answer as in Wolfram. Here's the outline of my solution 2. How is it? Stage 3: Max \(x_3\) $$f_3^{op}(s_3) = \max_{0 \le x_3 \le s_3} {x_3}$$ Stage 2: Min \(x_2\) $$f_2^{op}(s_2) = \min_{0 \le x_2 \le s_2} {(1-x_2) f_3^{op}(s_2+x_2)}$$ Stage 1: Max \(x_1\) $$f_1^{op}(s_1) = \max_{0 \le x_1 \le s_1} {(x_1) f_2^{op}(s_1 - x_1)}$$ where $$s_1 = 1$$ After we solve $$x_1^{op} = 2/3$$ we get $$x_2^{op} = s_2 = s_1 - x_1 = 1 - 2/3 = 1/3$$ $$x_3^{op} = s_2 + x_2 = 1/3 + 1/3 = 2/3$$ -
I think we have to minimise \(x_2\) and maximise everything else because the \(x_2\) part we want to maximise is \(1-x_2\), which would be done by minimising \(x_2\). Is that right? -
Why should we still have \(x_2 \color{red}{\le s_2}\)? My solution (whose answer agrees with Wolfram) seems to rely on \(x_2^{*} = s_2\). I was thinking that the negative coefficient of \(x_2\) in the constraint removes that. If not, what is the role of the negative coefficient of \(x_2\) (in the constraint, not objective function)? Just the \(s_2 \color{red}{+} x_2\)?
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