| 21. | The dimension of a vector space is the maximum size of a linearly independent subset.
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| 22. | And with these two exponents, we get two different expansions that are linearly independent?
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| 23. | Such a system is always linearly independent.
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| 24. | This can be done if and only if " S " is linearly independent.
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| 25. | In contrast to the positive-definite case, these vectors need not be linearly independent.
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| 26. | And since T is linearly independent and S spans, we get n \ geq m.
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| 27. | Either those two vectors are linearly independent, or, err, they're not.
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| 28. | As a consequence, eigenvectors of " different " eigenvalues are always linearly independent.
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| 29. | The trivial case of the empty family must be regarded as linearly independent for theorems to apply.
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| 30. | Is a solution of the same equation and is linearly independent from " y ".
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