Matrices
Class 12 Mathematics · Mathematics Part I · Chapter 3
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- Space weighted by unit marks
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- Space weighted by unit marks
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- Space weighted by unit marks
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
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- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
- Space weighted by unit marks
- Charts from the book's own data
- Traps corrected
Summary
A matrix is a rectangular array of numbers arranged in rows and columns; its order is \(m\times n\). This chapter develops matrix algebra: equality, addition, scalar multiplication and the row-by-column rule for matrix multiplication, where \((AB)_{ij}=\sum_k a_{ik}b_{kj}\). Matrix multiplication is associative and distributive but generally not commutative, so \(AB\neq BA\) in general. Special matrices are studied — diagonal, scalar, identity, symmetric (\(A^T=A\)) and skew-symmetric (\(A^T=-A\)). The transpose operation and its properties, such as \((AB)^T=B^TA^T\), are established. Elementary row and column operations are introduced and used to find the inverse of a matrix. Matrices provide a compact framework for representing and solving systems of linear equations.

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