M408M Learning Module Pages
Main page Chapter 10: Parametric Equations and Polar CoordinatesChapter 12: Vectors and the Geometry of SpaceChapter 13: Vector FunctionsChapter 14: Partial DerivativesLearning module LM 14.1: Functions of 2 or 3 variables:Learning module LM 14.3: Partial derivatives:Learning module LM 14.4: Tangent planes and linear approximations:Learning module LM 14.5: Differentiability and the chain rule:Learning module LM 14.6: Gradients and directional derivatives:Learning module LM 14.7: Local maxima and minima:Maxima, minima and critical pointsClassifying critical points Example problems Linear regression Learning module LM 14.8: Absolute maxima and Lagrange multipliers:Chapter 15: Multiple Integrals |
Maxima, minima and critical points
In one variable locating local extrema usually meant finding where f′(x)=0. In 2 variables we replace f′(x) by ∇f(x,y).
The crucial observation: Since ∇f always points uphill and −∇f always points downhill, a point where ∇f exists and isn't zero cannot be a local extremum. In other words, all local extrema are critical points. However, we have seen that not all critical points are local extrema. We have points of inflection, as in one dimension, and we can have saddle points. We can express this geometrically, in terms of the tangent plane to the surface z=f(x,y). The tangent plane exists and is horizontal precisely where ∇f=0. In one dimension, critical points were where the tangent line was horizontal or did not exist. In two dimensions, critical points are where the tangent plane is horizontal or does not exist. The previous graph of z=sinxsiny shows that ∇f(a,b)=0 and the tangent plane is horizontal at P,Q, and
R. Let's see in detail how this works algebraically to find all
critical points:
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