28. Linear Transformations
Linear transformations are fundamental in linear algebra, particularly in engineering applications where they provide systematic mappings of vectors while preserving linear structures. The chapter covers key aspects such as definitions, examples, matrices, compositions, invertibility, eigenvalues, and their practical applications in civil engineering contexts. The theories discussed facilitate a deeper understanding of solving linear systems and modeling physical phenomena accurately.
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What we have learnt
- Linear transformations map vectors in one vector space to another while preserving vector addition and scalar multiplication.
- The properties of kernel and image are vital in understanding the dimensions of linear transformations, encapsulated in the Rank-Nullity Theorem.
- Eigenvalues and eigenvectors serve crucial roles in structural dynamics and stress analysis, simplifying complex linear transformations.
Key Concepts
- -- Linear Transformation
- A function that maps vectors from one vector space to another while preserving the operations of vector addition and scalar multiplication.
- -- Kernel
- The set of vectors in the domain that are mapped to the zero vector in the codomain by a linear transformation.
- -- Image
- The set of vectors in the codomain that are the result of the linear transformations applied to vectors in the domain.
- -- RankNullity Theorem
- A theorem that relates the dimensions of the kernel and image of a linear transformation to the dimension of the vector space.
- -- Eigenvalues and Eigenvectors
- Eigenvalues represent the scaling factors when a linear transformation is applied to its eigenvectors, which are vectors that change only by a scalar factor.
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