Linear Interpolation


Linear interpolation is a mathematical technique used to estimate an unknown value between two known points on a straight line. By assuming that the line between the points is linear, the method calculates the value by proportionally scaling the distance from one point to another. It is widely used in fields like computer graphics, data analysis, and numerical simulations.

Key Takeaways

  1. Linear interpolation is a mathematical technique used to estimate an unknown value between two known data points, creating a straight line that connects those points.
  2. It is widely used in computer graphics, animation, and data analysis, as it provides a simple and computational efficient way to predict unknown values or fill gaps in data.
  3. While linear interpolation is versatile and easy to implement, it may not be accurate for complex or non-linear data sets, and higher-order interpolation methods might be required for more accurate results.


Linear interpolation is an important technology term because it is a fundamental mathematical method used to estimate unknown values within a given range, based on known data points.

This technique is widely applied across various fields such as computer graphics, engineering, and data analysis.

By using a simple linear equation, linear interpolation connects two known points to generate a continuous straight line.

This allows for the prediction of an approximate value at a desired position which can be extremely useful in designing computer animations, modeling physical systems, and constructing data-based predictive models.

Overall, linear interpolation serves as a valuable tool in simplifying complex problems, aiding researchers, and driving innovation in technology development.


Linear interpolation serves as a valuable tool in various fields, primarily where data points or sample values need to be estimated within a range of known values. This technique is used when there is a need to predict or compute values that fall between two known data points.

By assuming a straight line between the known data points, it provides a simple yet effective way to estimate unknown values. Linear interpolation proves its significance in fields such as computer graphics, mathematics, engineering, and finance, helping reduce complexities and providing an accurate representation of data trends.

For instance, in computer graphics, linear interpolation is employed to generate realistic visual effects through shading and texture mapping, making gradients appear smoother in rendered images. In finance, it can be utilized to estimate asset prices or interest rates for certain periods when only limited data is accessible.

A pragmatic approach with faster computational time, linear interpolation offers an easily comprehensible solution, albeit with limitations due to the simplistic assumption of a linear relationship between data points. Despite these limitations, its usefulness prevails across diverse applications when dealing with relatively simple data correlations.

Examples of Linear Interpolation

Linear interpolation is a method utilized in various fields to estimate unknown values based on known data points in a linear function. Here are three real-world examples of linear interpolation:

Weather Forecasting: Linear interpolation is used to estimate temperature, humidity, barometric pressure, or other parameters at specific times by interpolating data collected at regular intervals. For example, if weather data is collected at 6 a.m. and 12 p.m., linear interpolation could estimate the temperature at 9 a.m. based on the measured data.

Digital Image Processing: When resizing digital images, linear interpolation is used to approximate pixel values of the new image, based on the data from the original image. To reduce artifacts and maintain smooth transitions, this technique calculates pixel intensities for the resized image considering the neighboring pixel intensities from the original image.

Engineering and Physics: Linear interpolation is commonly used when a value in a continuous data set is needed but has not been experimentally measured. For example, a mechanical engineer may want to find the stress-strain relationship of a material at a specific elongation value, but only have experimental data at points before and after this value. Linear interpolation can be used to estimate the material behavior in between these experimentally known points.

Linear Interpolation FAQ

1. What is linear interpolation?

Linear interpolation is a method used to estimate values between two known data points. It forms a straight line between the points and calculates the value of a variable at any point on that line based on the relative distance between the known points. It is often used in mathematics, computer graphics, and data analysis.

2. What is the formula for linear interpolation?

The formula for linear interpolation is as follows:

y = y1 + [(x – x1) * (y2 – y1)] / (x2 – x1)

The formula takes the known data points (x1, y1) and (x2, y2) and estimates the value y at a target x position.

3. When should linear interpolation be used?

Linear interpolation should be used when you have two known data points, and you want to estimate a value between these points. It is a straightforward and relatively simple method, suitable for linear relationships between variables. However, it may not accurately represent curved or more complex relationships.

4. What are some applications of linear interpolation?

Linear interpolation has a wide range of applications, including:

  • Calculating missing values in a dataset
  • Computer graphics and animation to estimate color, position, and appearance between keyframes
  • Image and audio processing to estimate values in between known data points
  • Financial and investment scenarios to estimate the value of an asset between two periods

5. What are the limitations of linear interpolation?

While linear interpolation can provide a useful estimate of values between known data points, it has some limitations:

  • It assumes a linear relationship between the variables, which may not always be accurate.
  • It cannot accurately predict values outside the known data points (extrapolation).
  • It lacks the ability to represent curved or more complex relationships between variables.

Related Technology Terms

  • 1. Data points
  • 2. Interpolation formula
  • 3. Linear functions
  • 4. Extrapolation
  • 5. Piecewise continuous function

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