7️⃣ Introduction to Optimization — Slides

Mathematical Fundamentals · MS01-001-G

Learning Objectives

  • Compute an average rate of change between two points
  • Understand the instantaneous rate of change and the intuitive notion of the derivative
  • Apply derivative rules to affine and degree-2 polynomial functions
  • Find and classify maxima and minima to support decisions

1. Average Rate of Change

Formula

Between points \(a\) and \(b\):

\[\text{ARC} = \frac{f(b) - f(a)}{b - a}\]

  • Geometric meaning: slope of the secant line through \((a,\, f(a))\) and \((b,\, f(b))\)
  • Economic meaning: average variation of the quantity per unit change in the input

Example: revenue goes from €800 to €1,040 as sales go from 40 to 80 units.

\[\text{ARC} = \frac{1040 - 800}{80 - 40} = \frac{240}{40} = 6 \text{ €/unit}\]

2. The Derivative

The derivative \(f'(x)\) is the instantaneous rate of change — the limit of the ARC as the interval shrinks to a point.

Geometric meaning: slope of the tangent to the curve at \(x\).

Key Derivative Rules

Function Derivative
\(f(x) = ax + b\) \(f'(x) = a\)
\(f(x) = ax^2 + bx + c\) \(f'(x) = 2ax + b\)

3. Finding Extrema

Procedure

  1. Compute \(f'(x)\)
  2. Solve \(f'(x) = 0\) — the critical point is \(x^* = -\dfrac{b}{2a}\)
  3. Build a sign table for \(f'\)
  4. Read the nature of the critical point:
    • \(f'\) changes from \(+\) to \(-\)maximum
    • \(f'\) changes from \(-\) to \(+\)minimum

Worked Example — Profit Maximisation

Profit function: \(\pi(q) = -2q^2 + 120q - 800\)

Step 1 — Compute the derivative: \[\pi'(q) = -4q + 120\]

Step 2 — Solve \(\pi'(q) = 0\): \[q^* = 30 \text{ units}\]

Step 3 — Sign table: \(\pi' > 0\) for \(q < 30\), \(\quad \pi' < 0\) for \(q > 30\)

Step 4\(q^* = 30\) is a maximum.

\[\pi(30) = -2(900) + 120(30) - 800 = -1800 + 3600 - 800 = €1{,}000\]

Summary

  1. The derivative measures the instantaneous rate of change — it is the slope of the tangent at any point.

  2. Set \(f'(x) = 0\) to find critical points, then use a sign table to classify them as maxima or minima.

  3. For \(f(x) = ax^2 + bx + c\): \(a < 0\)maximum at \(x^*\); \(a > 0\)minimum at \(x^*\).

📖 Full Reference

→ Module 7: Introduction to Optimization