Topic 12

Critical Points & the Second Derivative Test

A critical point is where fx=0f_x = 0 and fy=0f_y = 0 at the same time. The second derivative test sorts each one using D=fxxfyy−fxy2D = f_{xx}f_{yy} - f_{xy}^2. If D<0D < 0, it is a . If D>0D > 0, it is a local min or max, by the sign of fxxf_{xx}. If D=0D = 0, the test says nothing.

4 kinds of question. Open the one you are stuck on.

1

Find and classify critical points using the second derivative test

e.g. “Find and classify all critical points of …”
  1. 1
    Compute fxf_x, fyf_y, fxxf_{xx}, fyyf_{yy} and fxyf_{xy}
  2. 2
    Solve fx=0f_x = 0 and fy=0f_y = 0 together

    Factor where you can: each factor equal to 00 is its own case. Otherwise solve one equation for a variable and substitute into the other. Expect several points.

  3. 3
    At each point compute D=fxxfyy−fxy2D = f_{xx}f_{yy} - f_{xy}^2
  4. 4
    Classify: D<0D < 0 saddle; D>0D > 0 min if fxx>0f_{xx} > 0, max if fxx<0f_{xx} < 0

    If D=0D = 0, the test is silent. Use the recipe for when the test fails, below.

Remember: Partials, solve, D, sign.

See it in a problem3 worked examples

Find and classify all critical points of f(x,y)=3x−x3−y2+4yf(x,y) = 3x - x^3 - y^2 + 4y.

  1. 1
    Compute fxf_x, fyf_y, fxxf_{xx}, fyyf_{yy} and fxyf_{xy}

    fx=3−3x2f_x = 3 - 3x^2, fy=−2y+4f_y = -2y + 4, fxx=−6xf_{xx} = -6x, fyy=−2f_{yy} = -2, fxy=0f_{xy} = 0.

  2. 2
    Solve fx=0f_x = 0 and fy=0f_y = 0 together

    3−3x2=0⇒x=±13 - 3x^2 = 0 \Rightarrow x = \pm 1;  −2y+4=0⇒y=2\ -2y + 4 = 0 \Rightarrow y = 2. Points (1,2)(1, 2) and (−1,2)(-1, 2).

  3. 3
    At each point compute D=fxxfyy−fxy2D = f_{xx}f_{yy} - f_{xy}^2

    (1,2)(1, 2): D=(−6)(−2)−0=12D = (-6)(-2) - 0 = 12.  (−1,2)\ (-1, 2): D=(6)(−2)−0=−12D = (6)(-2) - 0 = -12.

  4. 4
    Classify: D<0D < 0 saddle; D>0D > 0 min if fxx>0f_{xx} > 0, max if fxx<0f_{xx} < 0

    (1,2)(1, 2): D>0D > 0 and fxx=−6<0f_{xx} = -6 < 0, a local max.  (−1,2)\ (-1, 2): D<0D < 0, a saddle point.

Answer:

(1,2)(1, 2) local max; (−1,2)(-1, 2) saddle point

Common mistakes4
  • Dividing by a variable that can be 00. From 2y(x−1)=02y(x - 1) = 0 keep both cases, y=0y = 0 and x=1x = 1.
  • Looking at fxxf_{xx} when D<0D < 0. A negative DD is a saddle, whatever fxxf_{xx} is.
  • Calling D=0D = 0 a saddle. It means "no information".
  • Missing a point. Check that every case you split into gave its solutions.
2

Find the values of a constant that make a critical point a saddle, max or min

e.g. “Use the Second Derivative Test to find all values of the constant cc for which … has a saddle point at (0,0)(0,0)”
  1. 1
    Check the point is critical for every value of the constant
  2. 2
    Compute DD at the point, in terms of the constant
  3. 3
    Set the condition: D<0D < 0 saddle; D>0D > 0 plus the sign of fxxf_{xx}
  4. 4
    Solve for the constant; check D=0D = 0 separately

    At D=0D = 0 the test is silent. Look at ff itself: it is often a perfect square.

Remember: D in terms of the constant, then an inequality.

See it in a problem2 worked examples

Use the second derivative test to find all values of kk for which f(x,y)=2x2+kxy+8y2f(x,y) = 2x^2 + kxy + 8y^2 has a saddle point at (0,0)(0, 0).

  1. 1
    Check the point is critical for every value of the constant

    fx=4x+kyf_x = 4x + ky and fy=kx+16yf_y = kx + 16y are both 00 at (0,0)(0, 0), for every kk.

  2. 2
    Compute DD at the point, in terms of the constant

    fxx=4f_{xx} = 4, fyy=16f_{yy} = 16, fxy=kf_{xy} = k, so D=64−k2D = 64 - k^2.

  3. 3
    Set the condition: D<0D < 0 saddle; D>0D > 0 plus the sign of fxxf_{xx}

    Saddle: 64−k2<064 - k^2 < 0.

  4. 4
    Solve for the constant; check D=0D = 0 separately

    k2>64⇒∣k∣>8k^2 > 64 \Rightarrow |k| > 8. At k=±8k = \pm 8, D=0D = 0 and f=2(x±2y)2≥0f = 2(x \pm 2y)^2 \ge 0: a minimum, not a saddle.

Answer:

∣k∣>8|k| > 8

Common mistakes3
  • Forgetting fxxf_{xx} for a max or min: D>0D > 0 alone does not say which.
  • Writing k>8k > 8 for k2>64k^2 > 64. It is ∣k∣>8|k| > 8: k>8k > 8 or k<−8k < -8.
  • Including the boundary. At D=0D = 0 the test proves nothing, so it never "shows" a saddle.
3

Classify a critical point when the second derivative test fails

e.g. “D=0D = 0 at the critical point”
  1. 1
    Confirm the point is critical and D=0D = 0 there
  2. 2
    Write f(x,y)−f(a,b)f(x,y) - f(a,b) and simplify

    Factor or complete squares: squares and even powers are never negative.

  3. 3
    Test its sign along simple paths through the point

    Try the lines x=ax = a, y=by = b and y−b=m(x−a)y - b = m(x - a). If ff factors, also try a curve where the factors have opposite signs.

  4. 4
    Decide: one sign throughout gives min or max; both signs give a saddle

    Paths can prove a saddle. For a min or max, show the sign holds at every point near (a,b)(a, b).

Remember: When D is zero, look at f itself.

See it in a problem3 worked examples

Classify the critical point (0,0)(0, 0) of f(x,y)=x4+y2f(x,y) = x^4 + y^2.

  1. 1
    Confirm the point is critical and D=0D = 0 there

    fx=4x3f_x = 4x^3 and fy=2yf_y = 2y are both 00 at (0,0)(0, 0). There fxx=12x2=0f_{xx} = 12x^2 = 0, fyy=2f_{yy} = 2, fxy=0f_{xy} = 0, so D=0D = 0.

  2. 2
    Write f(x,y)−f(a,b)f(x,y) - f(a,b) and simplify

    f(x,y)−f(0,0)=x4+y2f(x,y) - f(0,0) = x^4 + y^2.

  3. 3
    Test its sign along simple paths through the point

    Both terms are ≥0\ge 0 at every point, and their sum is >0> 0 away from the origin.

  4. 4
    Decide: one sign throughout gives min or max; both signs give a saddle

    Never negative, so (0,0)(0, 0) is a local (in fact global) minimum.

Answer:

Local minimum

Common mistakes2
  • Concluding "saddle" because D=0D = 0. Zero means the test gave no answer.
  • Checking only straight lines. A function can rise along every line yet dip along a curve.
4

Solve a max/min word problem using critical points

e.g. “Find the most economical shape of a rectangular box …”
  1. 1
    Name the variables and write the quantity to optimize
  2. 2
    Use the constraint to eliminate one variable

    Solve the for one variable. That leaves a function of two variables. For a distance, minimize the square of the distance: same point, easier derivatives.

  3. 3
    Find the critical points of the new function
  4. 4
    Confirm it is the min or max you need

    Use the second derivative test. Then check the edges: if the quantity is worse there than at your one critical point, that point wins.

  5. 5
    Answer the question asked

    Dimensions, the point, the distance or the cost, with units.

Remember: Model, eliminate, critical point, confirm, answer.

See it in a problem3 worked examples

An open-top box must hold 6 m36\ \text{m}^3. The base costs 3 dollars per m2\text{m}^2 and the sides 2 dollars per m2\text{m}^2. Find the cheapest dimensions and their cost.

  1. 1
    Name the variables and write the quantity to optimize

    Base x×yx \times y, height zz. Cost C=3xy+2(2xz+2yz)C = 3xy + 2(2xz + 2yz), with xyz=6xyz = 6.

  2. 2
    Use the constraint to eliminate one variable

    z=6xyz = \dfrac{6}{xy}, so C=3xy+24x+24yC = 3xy + \dfrac{24}{x} + \dfrac{24}{y} for x,y>0x, y > 0.

  3. 3
    Find the critical points of the new function

    Cx=3y−24x2=0C_x = 3y - \dfrac{24}{x^2} = 0 and Cy=3x−24y2=0C_y = 3x - \dfrac{24}{y^2} = 0, so x2y=8=xy2x^2y = 8 = xy^2. Then x=yx = y and x3=8x^3 = 8: the point (2,2)(2, 2).

  4. 4
    Confirm it is the min or max you need

    Cxx=48x3=6C_{xx} = \dfrac{48}{x^3} = 6, Cyy=6C_{yy} = 6, Cxy=3C_{xy} = 3: D=27>0D = 27 > 0 and Cxx>0C_{xx} > 0, a min. C→∞C \to \infty at the edges, so it is the cheapest.

  5. 5
    Answer the question asked

    Base 2×22 \times 2 m, height z=64=1.5z = \dfrac{6}{4} = 1.5 m, cost 12+12+12=3612 + 12 + 12 = 36 dollars.

Answer:

2×2×1.52 \times 2 \times 1.5 m, costing 36 dollars

Common mistakes3
  • Forgetting to recover the eliminated variable: the box's height, the point's zz.
  • Stopping at a critical point without checking it is the max or min you were asked for.
  • Minimizing a distance with the square root still on. Square it first.

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