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--- title: "Disciplined Convex Programming" source: https://www.jemoka.com/posts/kbhdiciplined_convex_programming/ --- Specify objective as: minimize scalar convex expression maximize scalar concave expression and constraints: convex expr <= concave expr concave expr >= convex expr affine expr = affine expr curvatures of all expressions are DCP certified. We do this because then you can just subtract the expressions and you’ll have a good time. you certify DCP based on general composition rule that preserve convexity DCP is sufficient, not necessary Consider: \begin{equation} f\qty(x) = \sqrt{1+x^{2}} \end{equation} f1 = cp.sqrt(1 + cp.square(x)) is not DCP (because we put convex into concave) f1 = cp.norm2([1,x]) is DCP. These are identical.