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John Stillwell - Naive Lie Theory.pdf - Index of

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130 6 Structure <strong>of</strong> <strong>Lie</strong> algebras<br />

[X,E ij ]=<br />

⎛<br />

i j<br />

⎞<br />

−x 1 j x 1i<br />

−x 2 j x 2i<br />

. .<br />

i<br />

−x j1 −x j2 ··· 0 ··· 0 ··· −x jn<br />

.<br />

. .<br />

j<br />

x i1 x i2 ··· 0 ··· 0 ··· x in<br />

⎜<br />

⎟<br />

⎝<br />

. .<br />

⎠<br />

−x nj x ni<br />

(*)<br />

We now make a series <strong>of</strong> applications <strong>of</strong> formula (*) for [X,E ij ] to<br />

reduce a given nonzero X ∈ so(n) to a nonzero multiple <strong>of</strong> a basis vector.<br />

The result is the following theorem.<br />

Simplicity <strong>of</strong> so(n). For each n > 4, so(n) is a simple <strong>Lie</strong> algebra.<br />

Pro<strong>of</strong>. Suppose that I is a nonzero ideal <strong>of</strong> so(n), andthatX is a nonzero<br />

n × n matrix in I. We will show that I contains all the basis vectors E ij ,so<br />

I = so(n).<br />

In the first stage <strong>of</strong> the pro<strong>of</strong>, we <strong>Lie</strong> bracket X with a series <strong>of</strong> four<br />

basis elements to produce a matrix (necessarily skew-symmetric) with just<br />

two nonzero entries. The first bracketing produces the matrix X 1 =[X,E ij ]<br />

shown in (*) above, which has zeros everywhere except in columns i and j<br />

and rows i and j.<br />

For the second bracketing we choose a k ≠ i, j and form X 2 =[X 1 ,E jk ],<br />

which has row and column j <strong>of</strong> X 1 moved to the k position, row and column<br />

k <strong>of</strong> X 1 moved to the j position with their signs changed, and zeros where<br />

these rows and columns meet. Row and column k in X 1 =[X,E ij ] have<br />

at most two nonzero entries (where they meet row and column i and j),<br />

so row and column j in X 2 =[X 1 ,E jk ] each have at most one, since the

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