Multivariate calculus from first principles
Optimization in many dimensions begins exactly where it did in 1-D: find where the slope is zero. But now 'slope' is the whole gradient vector, so a critical point is where every partial derivative vanishes at once, ∇f = 0.
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▶ Critical Points in Rⁿ