Eigen value is a scalar value or set of scalar values that tells us how much a vector is stretched or shrunk when it does under linear transformation.

It is represented by the equation

T(x)=λx, where T is a linear transformation, x is a vector and λ is an eigen value.

Simply select the dimension of matrix you want to calculate the eigen values for.

You can choose one from 2x2, 3x3, 4x4 and 5x5 matrix.

After you select dimension, enter the matrix elements in the input fields.And click on get eigen values

Finding eigen values is a fun task to involve in. However, its not only a mathematical game you do for fun. Eigen values have wide range of applications in real life.

Vectors are used in fields like Artificial Intelligence, Network Analysis, Physics, Biology, Chemistry, Astronomy, Mechanical Engineering and even Finance & Economics

Any field which involves vector, involves linear transformation and hence eigen values.

Eigen values are not just a paper work, they solve a great problem in real world. And understanding it helps you to gain deeper insights into how things work in real life.

Images are processed as a vector of pixels by computer. Whenever a image is compressed, the scalar values of the vector (representing the image) are compressed.

Eigen value is a scalar value that tells how much a vector is stretched or shrunk when it goes under linear transformation. Eigen values can be 0 or non-zero. A vector having an eigen value of 0 is non-invertible.

Eigen vector is a vector that does not change its direction when linear transformation is applied on it. It only stretches or shrink. Eigen vectors cannot be 0, they are always non-zero vectors because zero vector don't have any direction

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