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The linear change dy=f′(x)dx approximates the actual output change for small dx.
Worked example 1
For y=x2 at x=2 with dx=0.01, find dy.
dy=f′(x)dx; f′(x)=2x
dy=2(2)(0.01)=251≈0.04
Answer:251≈0.04
Worked example 2
For y=x at x=3 with dx=0.02, find dy.
dy=f′(x)dx; f′(x)=2x1
dy=231(0.02)=20032≈0.00577
Answer:20032≈0.00577
Idea 2 of 3
Linear approximation
Use L(x)=f(a)+f′(a)(x−a) near a convenient base point a.
Worked example 1
Use linearization at x=4 to approximate 4.02.
f(4)=2; f′(4)=41
L(x)=2+4x−4
L(4.02)=2+40.02=200401≈2
Answer:200401≈2
Worked example 2
Use linearization at x=9 to approximate 9.04.
f(9)=3; f′(9)=61
L(x)=3+6x−9
L(9.04)=3+60.04=150451≈3.01
Answer:150451≈3.01
Idea 3 of 3
Propagating small input changes
Keep the original input in the derivative. The differential approximates error; it is not the exact difference.
Worked example 1
For y=x2 at x=2 with dx=0.01, find dy.
dy=f′(x)dx; f′(x)=2x
dy=2(2)(0.01)=251≈0.04
Answer:251≈0.04
Worked example 2
For y=x at x=3 with dx=0.02, find dy.
dy=f′(x)dx; f′(x)=2x1
dy=231(0.02)=20032≈0.00577
Answer:20032≈0.00577
Resource alignment: Larson & Edwards, Calculus of a Single Variable, Tenth Edition, §3.9,p. 231. Explanations, diagrams and numerical practice variants are original MathLab content aligned to this section.