Multivariate calculus from first principles
Some optimization problems are easy and some are hard, and one property draws the line: convexity. A convex function has a single bowl shape with no false bottoms, so finding a place where the gradient is zero means you've found the global minimum. No saddles, no local traps.
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▶ Convexity