How to Use the MINVERSE Function in Google Sheets


Google Sheets is a powerful tool that offers a wide range of functions to help users perform various tasks efficiently. One such function is MINVERSE, which is used for matrix inversion. In simpler terms, it helps in finding the inverse of a given matrix. This article will guide you through the process of using the MINVERSE function in Google Sheets.

When to Use the MINVERSE Function:

The MINVERSE function is used when you need to find the inverse of a matrix. In mathematical terms, the inverse of a matrix A is denoted as A<sup>-1</sup>. It is used in various fields including mathematics, engineering, and statistics.

How to insert the MINVERSE formula in Google Sheets

  • Type “=MINVERSE” or go to “Insert” → “Function” → “Array” → “MINVERSE”.
How To Insert The Minverse Formula In Google Sheets
How To Insert The Minverse Formula In Google Sheets



  • square_matrix – An array or range with an equal number of rows and columns representing a matrix whose multiplicative inverse will be calculated.


MINVERSE : General Usage

Step 1: Open Your Google Sheet:

Open Your Google Sheet
Open Your Google Sheet

Step 2: Select a Cell and Enter the Function

Select A Cell And Enter The Function
Select A Cell And Enter The Function

Step 3: Press Enter: Hit the Enter key to apply the function

Hit The Enter Key To Apply The Function
Hit The Enter Key To Apply The Function

Step 4: Summary of the above 3 steps

Summary Of The Above 3 Steps
Summary Of The Above 3 Steps

Tips for Optimization:

  1. Use Small Matrices Whenever Possible:
    • Smaller matrices require fewer computational resources. If your task allows it, try to work with smaller matrices.
  2. Avoid Using MINVERSE in Large Arrays:
    • Applying MINVERSE to a large range of cells can be resource-intensive. Consider breaking down the task into smaller, manageable chunks.
  3. Limit Complex Calculations:
    • If the matrices involved in your calculation have complex elements, consider simplifying them before applying the MINVERSE function.
  4. Precompute Determinants:
    • If you’re working with multiple matrices and need to calculate determinants, do so separately and reuse the results in the MINVERSE function. This reduces redundant computations.
  5. Minimize Use of Volatile Functions:
    • Volatile functions like INDIRECT or OFFSET can recalculate even when unrelated cells change. Minimize their usage to improve performance.
  6. Use Matrix Multiplication Instead of MINVERSE:
    • In some cases, instead of inverting a matrix and then multiplying, it may be more efficient to directly perform matrix multiplication using the MMULT function.
  7. Consider Using Array Formulas with MMULT:
    • In situations where you need to perform matrix operations involving multiple arrays, using array formulas with MMULT can be more efficient than MINVERSE.
  8. Optimize Adjacency Calculations:
    • If you’re manually calculating the adjugate (adjacency) of a matrix, make sure to do so efficiently to reduce unnecessary computations.
  9. Avoid Recalculation of Constant Matrices:
    • If a matrix is a constant and won’t change, consider precomputing its inverse outside of Google Sheets and then directly inputting the values.
  10. Use the Cache for Repeated Computations:
    • If you’re working with the same matrix multiple times, calculate the MINVERSE once and store the result in a separate cell. Then, use that cell in subsequent calculations.
  11. Check for Alternative Solutions:
    • Depending on your specific problem, there might be alternative methods or functions that could achieve the same result with better performance.
  12. Utilize Scripting for Complex Operations:
    • For highly complex matrix operations, consider using Google Apps Script to create custom functions or scripts tailored to your specific needs.

Real-World Application:

  1. Engineering and Physics:
    • In engineering, especially in fields like control systems and structural analysis, matrix inversion is used to solve systems of linear equations. This helps in determining unknowns in complex systems.
  2. Statistics and Data Analysis:
    • In multivariate statistics, matrix inversion is used in techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for dimensionality reduction and classification.
  3. Economics and Finance:
    • In economics, matrix inversion is applied in input-output analysis, which helps understand the interdependencies of different sectors in an economy. In finance, it’s used for portfolio optimization.
  4. Computer Graphics:
    • In computer graphics, matrix inversion plays a crucial role in transformations. It’s used to manipulate 3D objects in a 2D environment, enabling tasks like rotation, translation, and scaling.
  5. Machine Learning and Artificial Intelligence:
    • In machine learning algorithms like Linear Regression, matrix inversion is used to find the coefficients that best fit a model to a set of data points.
  6. Robotics:
    • In robotics, matrix inversion is utilized for tasks like inverse kinematics. It helps in determining the joint angles required to achieve a desired position and orientation of a robot’s end effector.
  7. Optimization Problems:
    • In various optimization problems, especially those involving linear programming, matrix inversion is a fundamental operation. It helps in finding the optimal solutions.
  8. Signal Processing:
    • In signal processing, matrix inversion is used for tasks like deconvolution, which helps in removing unwanted effects from a signal.
  9. Electrical Engineering:
    • In electrical circuits, matrix inversion is employed in solving networks with multiple elements and sources. This aids in determining current and voltage distributions.
  10. Environmental Modeling:
    • In environmental modeling, particularly in the study of fluid flow and transport phenomena, matrix inversion helps in solving complex systems of differential equations.
  11. Medical Imaging:
    • In medical imaging, matrix inversion is used in techniques like computed tomography (CT) reconstruction, where it helps in generating detailed cross-sectional images from X-ray projections.
  12. Game Development:
    • In 3D game development, matrix inversion is used extensively for tasks like camera transformations, which allow for realistic rendering of 3D scenes.


In conclusion, the MINVERSE function in Google Sheets is a powerful tool that allows users to efficiently perform matrix inversion. By understanding its syntax and how to apply it, you can streamline your data manipulation processes and tackle complex mathematical tasks with ease. Whether you’re working in mathematics, engineering, statistics, or other fields, the ability to find the inverse of a matrix is invaluable.

Remember that the determinant and adjugate play a crucial role in the calculation of the inverse matrix. Additionally, it’s important to note that the MINVERSE function can only be applied to square matrices.


Q1: What happens if I try to invert a non-square matrix?
The MINVERSE function can only be used on square matrices, meaning matrices with the same number of rows and columns. If you attempt to use it on a non-square matrix, Google Sheets will return an error.
Q2: Can I use cell references instead of an actual matrix?
Yes, you can use cell references as the ‘array’ argument in the MINVERSE function. This allows for dynamic matrix inversion based on the values in your spreadsheet.
Q3: What is the significance of the inverse of a matrix?
The inverse of a matrix has various applications in mathematics and other fields. It is used to solve systems of linear equations, compute eigenvalues and eigenvectors, and perform many other mathematical operations. In practical terms, it finds applications in engineering, physics, computer graphics, and more.
Q4: Is there a limit to the size of the matrix that can be inverted?
Yes, there is a practical limit to the size of the matrix that can be inverted, which depends on the computational capacity of your computer and the capabilities of Google Sheets. Extremely large matrices may lead to performance issues.
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Deepak Vishwakarma


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