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Applications of Derivatives - Examples and Constructions

Optimization problems seek to maximize or minimize a quantity subject to constraints. These problems appear throughout science, engineering, economics, and everyday life, making them among the most practical applications of calculus.

DefinitionOptimization Problem Framework

A typical optimization problem involves:

  1. Objective function: The quantity to be maximized or minimized, f(x)f(x)
  2. Constraint(s): Conditions that restrict the domain, often given as equations or inequalities
  3. Critical point analysis: Finding where f(x)=0f'(x) = 0 or doesn't exist
  4. Verification: Confirming which critical point gives the desired extremum
ExampleClassic Box Problem

A rectangular box with a square base and open top is to have a volume of 32 cubic meters. Find the dimensions that minimize the amount of material used.

Let xx = side length of the square base, hh = height.

Constraint: V=x2h=32V = x^2h = 32, so h=32x2h = \frac{32}{x^2}

Surface area (objective function): S(x)=x2+4xh=x2+4x32x2=x2+128xS(x) = x^2 + 4xh = x^2 + 4x \cdot \frac{32}{x^2} = x^2 + \frac{128}{x}

Domain: x>0x > 0 (physical constraint)

S(x)=2x128x2=2x3128x2S'(x) = 2x - \frac{128}{x^2} = \frac{2x^3 - 128}{x^2}

Setting S(x)=0S'(x) = 0: 2x3=128    x3=64    x=42x^3 = 128 \implies x^3 = 64 \implies x = 4

S(x)=2+256x3,S(4)=2+25664=6>0S''(x) = 2 + \frac{256}{x^3}, \quad S''(4) = 2 + \frac{256}{64} = 6 > 0

So x=4x = 4 gives a minimum. Then h=3216=2h = \frac{32}{16} = 2.

Answer: Base 4×44 \times 4 meters, height 22 meters.

ExampleEconomic Application - Maximizing Revenue

A company can sell xx items per week at a price of p(x)=2000.5xp(x) = 200 - 0.5x dollars each. The cost of producing xx items is C(x)=1000+40xC(x) = 1000 + 40x dollars. How many items should be produced to maximize profit?

Revenue: R(x)=xp(x)=x(2000.5x)=200x0.5x2R(x) = xp(x) = x(200 - 0.5x) = 200x - 0.5x^2

Profit: P(x)=R(x)C(x)=200x0.5x2100040x=0.5x2+160x1000P(x) = R(x) - C(x) = 200x - 0.5x^2 - 1000 - 40x = -0.5x^2 + 160x - 1000

P(x)=x+160P'(x) = -x + 160

Setting P(x)=0P'(x) = 0: x=160x = 160

P(x)=1<0P''(x) = -1 < 0

So x=160x = 160 gives a maximum. The company should produce 160 items per week.

Remark

Steps for solving optimization problems:

  1. Draw a diagram if applicable
  2. Identify the quantity to optimize and express it as a function of one variable
  3. Use constraints to eliminate extra variables
  4. Find the domain of the objective function
  5. Compute the derivative and find critical points
  6. Use the first or second derivative test to classify extrema
  7. Check endpoints if the domain is closed
  8. Answer the question in context
ExampleGeometric Optimization

Find the point on the parabola y=x2y = x^2 closest to the point (0,1)(0, 1).

Distance from (x,x2)(x, x^2) to (0,1)(0, 1): D(x)=x2+(x21)2D(x) = \sqrt{x^2 + (x^2 - 1)^2}

To simplify, minimize D2D^2 instead: f(x)=x2+(x21)2=x2+x42x2+1=x4x2+1f(x) = x^2 + (x^2 - 1)^2 = x^2 + x^4 - 2x^2 + 1 = x^4 - x^2 + 1

f(x)=4x32x=2x(2x21)f'(x) = 4x^3 - 2x = 2x(2x^2 - 1)

Critical points: x=0,±12x = 0, \pm\frac{1}{\sqrt{2}}

f(x)=12x22f''(x) = 12x^2 - 2

At x=0x = 0: f(0)=2<0f''(0) = -2 < 0 (local maximum) At x=±12x = \pm\frac{1}{\sqrt{2}}: f(12)=12122=4>0f''\left(\frac{1}{\sqrt{2}}\right) = 12 \cdot \frac{1}{2} - 2 = 4 > 0 (local minimum)

The closest points are (±12,12)\left(\pm\frac{1}{\sqrt{2}}, \frac{1}{2}\right), each at distance 12\frac{1}{\sqrt{2}} from (0,1)(0, 1).

Optimization leverages the power of calculus to solve practical problems that would be intractable through trial and error or geometric reasoning alone.