Google Sheets

How to Use the MMULT Function in Google Sheets


Google Sheets is a powerful tool that offers a wide range of functions to help users perform various calculations and data analysis. One such function that comes in handy when dealing with matrices is the MMULT function. The MMULT function allows you to perform matrix multiplication in Google Sheets, which is a fundamental operation in linear algebra and has many applications in fields like mathematics, engineering, and data science.

In this article, we will explore what the MMULT function is, when to use it, its syntax, provide examples, and guide you through a step-by-step approach to using it effectively. By the end, you’ll have a solid understanding of how to leverage the MMULT function to handle matrix multiplication in your Google Sheets.

When to Use the MMULT Function:

The MMULT function is used when you have two matrices and need to perform matrix multiplication. This operation is essential for various mathematical and analytical tasks, including solving systems of linear equations, finding eigenvalues and eigenvectors, and conducting transformations in computer graphics.

How to insert the MMULT formula in Google Sheets

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


=MMULT(matrix1, matrix2)

  • matrix1 – The first matrix in the matrix multiplication operation, represented as an array or range.
  • matrix2 – The second matrix in the matrix multiplication operation, represented as an array or range.


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

Tips for Optimization:

Sure! Here are some tips for optimizing the use of the MMULT function in Google Sheets, presented in bullet points for easy reference:

  1. Minimize Matrix Size:
    • Keep matrices as small as possible to improve performance. Large matrices can slow down calculations.
  2. Avoid Excessive Nesting:
    • Limit the number of MMULT functions nested within each other. Excessive nesting can lead to complex and hard-to-debug formulas.
  3. Transpose Matrices if Needed:
    • If the number of rows and columns in the matrices are compatible, consider transposing one of them to optimize the multiplication process.
  4. Use Named Ranges:
    • Define named ranges for your matrices. This can make your formulas more readable and easier to manage.
  5. Limit Decimals:
    • Round or truncate decimals to a reasonable precision. This can help reduce computational overhead.
  6. Avoid Redundant Calculations:
    • If you’re performing the same matrix multiplication multiple times, calculate it once and reference the result in subsequent calculations.
  7. Check for Empty Cells:
    • Ensure that there are no empty cells within the matrices, as this can lead to unexpected results.
  8. Optimize Data Entry:
    • Enter matrix data efficiently and accurately. Small mistakes can lead to incorrect results.
  9. Consider Alternative Approaches:
    • For complex operations involving large matrices, consider if there are alternative methods or tools that may be more efficient.
  10. Monitor Performance:
    • Keep an eye on the performance of your Google Sheets document. If you notice sluggishness, it may be due to complex MMULT operations.
  11. Utilize Parallelization:
    • If possible, break down large matrix operations into smaller ones and perform them separately. Then, combine the results.
  12. Limit Conditional Formatting:
    • If conditional formatting is applied to cells containing matrices, it can potentially slow down calculations. Use it judiciously.
  13. Clear Unnecessary Formulas:
    • Remove any redundant or unnecessary formulas. This can help improve the overall efficiency of your spreadsheet.
  14. Consider Scripting:
    • For extremely complex matrix operations, consider using Google Apps Script to implement custom solutions.
  15. Regularly Test Formulas:
    • Test your MMULT formulas with small, known matrices to ensure they are working as expected before applying them to larger datasets.

Real-World Application:

The MMULT function in Google Sheets, which allows for matrix multiplication, finds applications in various real-world scenarios across different industries. Here are a few examples:

  1. Financial Analysis:
    • Portfolio Performance Evaluation: In finance, MMULT can be used to calculate the overall performance of a portfolio containing various financial instruments.
  2. Engineering and Physics:
    • Structural Analysis: Engineers use matrix multiplication to analyze the behavior of structures under different loads, helping in the design and optimization of buildings and bridges.
  3. Economics and Market Research:
    • Input-Output Analysis: Economists use matrix multiplication to study the interdependence between different sectors of an economy, helping in policy formulation.
  4. Supply Chain Management:
    • Transportation and Distribution Planning: MMULT can be used to optimize transportation routes and distribution channels for a more efficient supply chain.
  5. Image Processing and Computer Graphics:
    • Transformation Matrices: In computer graphics, matrices are used to perform transformations such as translation, rotation, and scaling.
  6. Statistics and Data Science:
    • Linear Regression Analysis: In regression analysis, MMULT can be used to efficiently calculate the coefficients of a linear regression model.
  7. Machine Learning:
    • Neural Networks: In deep learning, matrix multiplication is a fundamental operation used in the forward and backward passes of neural networks.
  8. Genetics and Bioinformatics:
    • Genomic Sequencing: MMULT can be used in bioinformatics to analyze genetic data and identify patterns or relationships.
  9. Environmental Modeling:
    • Climate Modeling: Climate scientists use matrices to represent complex models of climate systems, helping in predicting future climate trends.
  10. Operations Research:
    • Linear Programming: MMULT is used in solving linear programming problems, which arise in optimizing resources in various industries.
  11. Social Network Analysis:
    • Centrality Measures: In sociology and network theory, matrices are used to calculate centrality measures to identify influential nodes in a network.
  12. Game Theory:
    • Strategic Interaction: Game theorists use matrices to represent strategic interactions between players in various scenarios.
  13. Electric Circuits:
    • Electrical Engineering: MMULT can be used to analyze electrical circuits, determining currents and voltages at different nodes.
  14. Chemical Kinetics:
    • Chemistry and Chemical Engineering: Matrices are used to represent and solve systems of differential equations in chemical reactions.
  15. Biomedical Engineering:
    • Medical Imaging Reconstruction: MMULT is used in tasks like image reconstruction from projections in medical imaging.


The MMULT function in Google Sheets provides a powerful tool for performing matrix multiplication, a fundamental operation in linear algebra. By understanding its syntax and how to use it effectively, you can leverage this function for various mathematical and analytical tasks.


Q1: Can I use MMULT for non-square matrices?
Yes, you can use the MMULT function for matrices of different dimensions as long as the number of columns in the first matrix is equal to the number of rows in the second matrix.
Q2: What happens if the matrices are not compatible for multiplication?
If the matrices are not compatible for multiplication (i.e., the number of columns in the first matrix is not equal to the number of rows in the second matrix), Google Sheets will return an error.
Q3: Are there any limitations to the size of matrices that MMULT can handle?
Google Sheets has a limitation on the number of cells it can process, so very large matrices may lead to performance issues. It’s recommended to break down large computations into smaller steps if needed.

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