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Cheatsheet: Higher-order derivatives

A derivative is a function, so differentiate again:

f -> f' -> f'' -> f''' -> f^(4) -> ...
Leibniz: d²f/dx², d³f/dx³, ...
position s(t) -> velocity s'(t) -> acceleration s''(t) -> jerk s'''(t)

F = ma: force = mass times the second derivative of position.

f' is the slope; f'' is how the slope changes (curvature):

f''Shape
> 0Cups upward (smiling); slope increasing
< 0Cups downward (frowning); slope decreasing
= 0 with sign changeInflection point (curvature flips)

At a critical point (f'(x) = 0):

  • f''(x) > 0 -> local minimum (cup holds water)
  • f''(x) < 0 -> local maximum
  • f''(x) = 0 -> inconclusive; look closer
ff'f''Reading
-16t^2+64t+32-32t+64-32acceleration = gravity (constant)
x^3 - 3x3x^2-36xmin at x=1 (-2), max at x=-1 (2), inflection at 0
sin xcos x-sin xf'' = -f (oscillation equation)
e^xe^xe^xevery derivative is e^x

Free-fall at t=2: velocity 0 (peak), height 96 ft, acceleration -32.

Second-order (curvature) information drives optimization:

  • Newton’s method uses the Hessian (matrix of second derivatives) to converge faster than gradient descent on well-behaved problems.
  • Adam keeps a running estimate of how the gradient changes (an informal curvature signal); K-FAC approximates the Hessian.
  • Loss-landscape analysis (saddle points, flat basins, sharp ridges) is second-derivative geometry.
  • f” is the slope. No, f' is the slope; f'' is how the slope changes.
  • Flipping the second-derivative test. f'' > 0 = minimum (cup holds water), f'' < 0 = maximum.
  • f'' = 0 always means inflection. Only if the curvature actually changes sign.
  • Stopping at velocity. Second derivative of position is acceleration, not velocity.

The second derivative is the rate the slope changes: acceleration in time, curvature on a graph, and the test that tells a minimum (f''>0) from a maximum (f''<0).