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Subsequent work generalized W to nonbinary-bounded variables, and showed that the normalization W solves not only the bounds problem (like the concentration index for an unbounded variable, W lies between −1 and +1) but also the mirror problem (in the Kjellsson et al. pills example, both the initial attainment and initial shortfall distributions give the same [absolute] value of W, namely 0.133).
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[1405, 1397] The krb5_principal buffer bounds problems [MITKRB5-SA-2003-005] have been fixed.
But the good news is that is a bounded problem.
I argue that some progress can be made by (very deliberately) thinking algorithmically about the lower bound problem.
Much significant work on lower bounds in complexity theory has arisen from reconsidering the basic lower bound problem in an algorithmic light.
The solution of energy-gain bounding problem, being robust, shows an improved performance of the identification method.
I should add that when I talk about the liquidity trap, I mean the zero-lower-bound problem that arises in IS-LM.
We identify the resulting upper bound problem as a second-order conical optimization problem, for which advanced optimization algorithms became recently available.
And the "muscle-bound" problem is real, too: to the extent that having too much money means venture capitalists wait to enter until later rounds of financing, the value that they add is reduced.
You could use discretionary fiscal policy, a k a stimulus, to boost demand; you could use unconventional monetary policy to depress interest rate spreads and/or raise expected inflation, helping get past the zero lower bound problem.
But board and PC games are tightly bounded problem spaces with set rules and limits.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com