Stretching – Vertical & Horizontal Stretch of Functions

Learn vertical and horizontal stretching transformations of function graphs. Key for understanding scaling behavior.
🔑

Definition of Vertical Stretching & Compression

Vertical stretching and compressing are transformations that change the height or amplitude of a function by multiplying all y-values by a constant factor. Stretching makes graphs taller (|a| > 1), while compressing makes them shorter (0 < |a| < 1). These transformations model amplitude changes in waves, scaling effects in physics, and proportional adjustments in real-world applications while preserving the function's basic shape and x-coordinates.

SymbolDescription
g(x) = a · f(x)The general formula for a vertical transformation, where g(x) is the transformed function.
aThe scaling factor, a constant that determines the stretch, compression, and reflection.
f(x)The original or 'parent' function before any transformation is applied.
|a| > 1Condition for a vertical stretch. The graph becomes taller.
0 < |a| < 1Condition for a vertical compression. The graph becomes shorter.
Sign of aDetermines reflection. If 'a' is negative, the function is also reflected across the x-axis.
(x, y) → (x, ay)The point mapping rule. Each point's x-coordinate stays the same, while its y-coordinate is multiplied by the scaling factor 'a'.
📜

Key Formulas

\[ g(x) = a \cdot f(x) \]
General Form
\[ |a| > 1 \]
Condition for Vertical Stretch
\[ 0 < |a| < 1 \]
Condition for Vertical Compression
\[ a < 0 \]
Condition for Reflection across x-axis
\[ (x, y) \rightarrow (x, ay) \]
Point Transformation Rule
🖼️

Conceptual Diagram

a·f(x) a·f(x): vertical a>1 → stretch 0<a<1 → compress f(bx): horizontal
Stretching and compression: a·f(x) scales vertically (a>1 stretches, 0<a<1 compresses); f(bx) scales horizontally (b>1 compresses, 0<b<1 stretches).

A conceptual diagram shows a parent function, such as y = f(x), as a standard curve. A second curve, representing a vertical stretch where |a| > 1, is shown as a taller version of the original, with every point pulled vertically away from the x-axis. A third curve, representing a vertical compression where 0 < |a| < 1, is shown as a shorter, flatter version, with every point pushed vertically toward the x-axis. Dashed lines connect points (x, y) on the original curve to their corresponding points (x, ay) on the transformed curves, highlighting that x-coordinates remain fixed.

⚙️

Properties of Vertical Transformations

Vertical transformations have several key properties that affect the features of a function's graph.

  • Y-Coordinate Scaling: All y-values of the function are multiplied by the constant factor 'a', while all x-values remain unchanged. This creates a uniform vertical scaling.
  • Domain Preservation: The domain (the set of all possible x-values) of the function remains completely unchanged.
  • Range Scaling: The range (the set of all possible y-values) is scaled by the factor |a|.
  • X-Intercepts: The x-intercepts (where y=0) remain unchanged, because a * 0 = 0.
  • Y-Intercept: The y-intercept is scaled by the factor 'a', changing from f(0) to a * f(0).
  • Reflection: If the factor 'a' is negative, the transformation includes a reflection across the x-axis in addition to the scaling.
🔍

Derivation of the Point Transformation Rule

We can derive the rule for how individual points are transformed by starting with the definition of the transformation.

\[ g(x) = a \cdot f(x) \]
1. Start with the definition of the vertical transformation.

Let (x₀, y₀) be an arbitrary point on the graph of the original function, f(x). By definition, this means that y₀ = f(x₀).

Now, let's find the corresponding point on the graph of the new function, g(x). This new point will have the same x-coordinate, x₀, and a new y-coordinate, y₁, which is equal to g(x₀).

\[ y_1 = g(x_0) = a \cdot f(x_0) \]
2. Express the new y-coordinate in terms of the transformation.

Since we know that y₀ = f(x₀), we can substitute y₀ into the equation for y₁.

\[ y_1 = a \cdot y_0 \]
3. Substitute the original y-coordinate.

Thus, the original point (x₀, y₀) is mapped to the new point (x₀, y₁), which is (x₀, a·y₀). This confirms the general rule for point transformation.

\[ (x, y) \rightarrow (x, ay) \]
4. Final Point Transformation Rule
✏️

Worked Example

Given the function f(x) = x² - 3 and the point P(2, 1) on its graph. Find the equation of the new function g(x) after a vertical stretch by a factor of 4, and find the coordinates of the transformed point P'.
  1. The original function is f(x) = x² - 3.
  2. The vertical stretch factor is a = 4.
  3. Apply the transformation formula: g(x) = a · f(x).
  4. Substitute the function and factor: g(x) = 4 · (x² - 3).
  5. Distribute the factor to find the new equation: g(x) = 4x² - 12.
  6. To find the new point P', apply the point transformation rule (x, y) → (x, ay) to P(2, 1).
  7. The new coordinates are (2, 4 × 1), which simplifies to (2, 4).
The transformed function is g(x) = 4x² - 12, and the new point is P'(2, 4).
🧮

Try It

🚀

Applications

Physics & Acoustics

Physicists use vertical scaling to model changes in wave amplitude, variations in sound intensity, vibration analysis, and signal amplification in acoustic systems.

Economics & Finance

Economists apply vertical transformations for inflation adjustments, scaling financial models, proportional cost analysis, and modeling market volatility.

Engineering & Design

Engineers use vertical scaling for calculating structural loads, analyzing material stress, proportional design scaling, and applying safety factors in construction.

Computer Graphics & Animation

Graphics designers apply vertical transformations for object scaling, creating animation effects like bouncing or squashing, proportional resizing, and adjusting visual perspective.

🌍

Real-World Examples

An audio signal is modeled by the function f(t) = sin(t), which has a maximum amplitude of 1. A sound engineer uses an amplifier to double the signal's intensity. What is the function g(t) for the amplified signal and what is its new maximum amplitude?
  1. The original signal function is f(t) = sin(t).
  2. Doubling the intensity is a vertical stretch with a factor of a = 2.
  3. The new function is g(t) = a · f(t) = 2 · sin(t).
  4. The original maximum amplitude was 1. The new maximum amplitude is a × 1 = 2 × 1 = 2.
The amplified signal is modeled by g(t) = 2sin(t), and its new maximum amplitude is 2.
A baker's weekly profit from selling cakes is modeled by P(x) = -5x² + 60x - 100, where x is the price per cake in dollars. After improving the recipe, the profit at every price point increases by 50%. What is the new profit model P_new(x)?
  1. The original profit function is P(x) = -5x² + 60x - 100.
  2. A 50% increase corresponds to multiplying by 1.5. This is a vertical stretch with a factor of a = 1.5.
  3. The new profit model is P_new(x) = 1.5 · P(x) = 1.5(-5x² + 60x - 100).
  4. Distribute the factor: P_new(x) = -7.5x² + 90x - 150.
The new profit model is P_new(x) = -7.5x² + 90x - 150.
🏞️

Real-World Scenarios

a>1 stretch | a<1 compress
Image Zoom and Scaling in Photography
Zooming into a photo applies a vertical stretch a·f(x,y) to pixel intensity — no, wait: geometric scaling applies f(x/b, y/b), a horizontal stretch/compression. Zoom factor 2× maps each pixel at (x,y) to (2x, 2y) — stretching the image. Image processing software (Photoshop, GIMP) implements this as a·f(x) for intensity rescaling (contrast) and f(bx) for geometric scaling, and both are used when resizing images for print vs screen.
A·sin(ωt): amplitude stretch
Audio Amplification and Gain
An audio amplifier multiplies the input signal V_in(t) by gain A: V_out(t) = A·V_in(t). This is a vertical stretch — every peak and trough is amplified by factor A. Guitar amplifiers, hearing aids, and PA systems all apply this stretch transformation. Setting gain too high (over-stretch) causes clipping when peaks exceed the supply voltage, creating the characteristic distortion sound of an overdriven guitar amplifier.
f(2x): double freq (compress)
Frequency Scaling in DSP and Music
Transposing audio up one octave doubles the frequency: f_out(t) = f_in(2t) — a horizontal compression by factor 2. Playing audio at 2× speed applies the same transformation. Digital pitch-shifting in Auto-Tune and studio DAWs uses f(b·t) to scale frequency, and time-stretching algorithms (used in podcast speedup without pitch change) decouple time-stretch from frequency-stretch using sophisticated inverse-transform pairs.

Audio Engineering

When a sound engineer adjusts the volume on a mixing board, they are applying a vertical stretch or compression to the sound wave's function. Increasing the volume stretches the wave, making it taller (higher amplitude), while decreasing the volume compresses it, making it shorter (lower amplitude).

Computer Graphics

In graphic design software, when a user resizes an object vertically without changing its width, they are performing a vertical stretch or compression. This allows artists to make characters or elements appear taller and thinner or shorter and wider while maintaining their horizontal position.

Architectural Scaling

An architect might design a blueprint for a decorative archway. If the client wants the same design but taller to fit a larger entrance, the architect applies a vertical stretch to the mathematical function describing the arch, increasing its height without altering its width.

📚

Types of Vertical Transformations

Condition on 'a'Transformation TypeEffect on Graph
a > 1Vertical StretchBecomes taller; points move away from the x-axis.
0 < a < 1Vertical CompressionBecomes shorter; points move toward the x-axis.
a < -1Vertical Stretch with ReflectionFlipped over the x-axis and becomes taller.
-1 < a < 0Vertical Compression with ReflectionFlipped over the x-axis and becomes shorter.
a = -1ReflectionFlipped over the x-axis with no change in height.
a = 1Identity (No change)The graph remains identical.
a = 0CollapseThe entire graph collapses into the x-axis (y=0).
⚠️

Common Mistakes

⚠️ Confusing Vertical and Horizontal Transformations: A common error is applying the scaling factor to the x-coordinate. Remember, `a · f(x)` affects the output (y-values), while a horizontal transformation `f(bx)` affects the input (x-values).
⚠️ Ignoring Reflection for Negative 'a': Students often focus only on the magnitude of 'a' for stretching or compressing and forget that a negative sign also causes a reflection across the x-axis. For `g(x) = -2f(x)`, the graph is both stretched by a factor of 2 AND flipped vertically.
⚠️ Incorrect Order of Operations: When combining transformations like `g(x) = a · f(x) + k`, it's crucial to perform the stretch/compression (multiplication) *before* the vertical shift (addition). Applying the shift first and then scaling will lead to an incorrect result.
🚀

Study Strategy

1 🧠 Grasp the Core Concepts
  • Review the 'Definition of Vertical Stretching & Compression' to distinguish between a stretch (a > 1) and a compression (0 < a < 1).
  • Study the 'Conceptual Diagram' to visualize how the factor 'a' pulls the graph away from or pushes it towards the x-axis.
  • Read 'Properties of Vertical Transformations' to understand how features like y-intercepts and turning points are affected while x-intercepts remain fixed.
  • Compare vertical transformations with the 'Related Transformations' like horizontal ones to solidify your understanding of their unique effects.
2 ✍️ Memorize the Formulas
  • Write down the main transformation `y = a * f(x)` repeatedly, labeling 'a' as the vertical stretch/compression factor.
  • Focus on the 'Derivation of the Point Transformation Rule', memorizing that any point (x, y) on the original graph becomes (x, ay) on the new graph.
  • Create flashcards for the 'Key Formulas', quizzing yourself on the point transformation rule for both stretching and compression.
  • Recite the rule aloud: 'To vertically stretch or compress a function, you multiply only the y-coordinates by the factor a.'
3 🏋️ Practice with Examples
  • Follow the 'Worked Example' step-by-step, then cover the solution and solve it independently to test your recall.
  • Actively review the 'Common Mistakes' section and attempt practice problems specifically designed to test those pitfalls.
  • Apply the point transformation rule `(x, y) -> (x, ay)` to key points of parent functions like y=x², y=|x|, and y=sin(x).
  • Use graphing software to check your hand-drawn sketches of transformed functions and visually confirm the effect of different 'a' values.
4 🌍 Apply to Real-World Scenarios
  • Analyze the 'Real-World Examples', such as signal amplification, and explain in your own words how the stretching formula applies.
  • Read the 'Applications' section (e.g., in physics or economics) and try to formulate a new problem based on one of the contexts.
  • Examine the given 'Real-World Scenarios' and determine the value and meaning of the stretch factor 'a' in each case.
  • Create your own simple real-world problem, such as modeling a sales forecast adjusted for market optimism, and solve it using the formula.
By systematically understanding, memorizing, practicing, and applying, you can master function transformations and model the world mathematically.

Frequently Asked Questions

×

×