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One of the skillsets that the AI phenomenon has quietly boosted is the capability of explaining yourself: putting into words what you actually want, and providing methodologies that can unambiguously verify the implementation against your expectations.
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Most of the time when I was reading propositions/theorems/corollaries stating necessary-but-not-sufficient conditions I used to feel almost defrauded: you’re selling me something that sounds useful but actually tells me nothing about the statement I’m try…
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If you have ever tried to minimize a function with gradient descent, you probably noticed that some functions are a joy to optimize and others are a nightmare.
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In a universe of \(n\) assets and \(m\) trading agents, we consider a barter exchange problem in which the \(j\)-th agent has an initial endowment of \(\Delta_{j}\geq0\) units of the \(i\)-th asset and he is willing to sell it for at least \(\Lambda_{j}\g…
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Wip!
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One of the first things I realized by approaching convex analysis is the importance of notation.