# Solving the matrix vector equation | Matrices | Precalculus | Khan Academy | Summary and Q&A

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April 11, 2014
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Solving the matrix vector equation | Matrices | Precalculus | Khan Academy

## TL;DR

Matrix equations can be used to represent systems of equations, and by finding the inverse of the coefficient matrix, we can solve for the unknown variables.

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### Q: How can matrix equations be used to solve systems of equations?

Matrix equations provide a compact representation of systems of equations by using matrices for coefficients, unknown variables, and constants. By finding the inverse of the coefficient matrix, we can solve for the unknown variables.

### Q: What is the role of the inverse matrix in solving matrix equations?

The inverse matrix allows us to solve matrix equations by canceling out the coefficient matrix on one side of the equation. We can multiply both sides of the equation by the inverse matrix, which results in isolating the unknown variables on one side.

### Q: How is the inverse matrix calculated?

To find the inverse matrix, we need to calculate the determinant of the coefficient matrix. Then, we apply the adjoint operation, which involves swapping the top left and bottom right elements and negating the sign of the top right and bottom left elements.

### Q: Why is thinking in terms of matrix equations important?

Thinking in terms of matrix equations is useful in computational problems where the left-hand side remains the same, but there are varying right-hand side values. By computing the inverse once and multiplying it with different right-hand side values, we can obtain multiple solutions efficiently.

## Summary & Key Takeaways

• Matrix equations can represent systems of equations by using matrices to represent coefficients, unknown variables, and the right-hand side.

• If the coefficient matrix is invertible, we can multiply both sides of the equation by the inverse matrix to solve for the unknown variables.

• Finding the inverse matrix involves calculating the determinant of the coefficient matrix and applying the adjoint operation.