For any positive integer , the matrix has components Define the quantity by the formula where denotes the trace of the matrix . It can be shown that the infinite sum converges to the rational limit where are coprime positive integers. Find the value of .
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Let F n ( t ) be the characteristic polynomial of A ( n ) . In the first round of this question, I showed that F n ( t ) = ( t + n ( n − 1 ) 1 ) F n − 1 ( t ) + t d e t B n − 1 ( t ) n ≥ 3 where B n − 1 ( t ) is the ( n − 1 ) × ( n − 1 ) matrix that only differs from t I n − 1 − A n − 1 by having − n − 1 1 , instead of t − n − 1 1 , in the bottom right corner. This, together with the initial values F 1 ( t ) = t − 1 , F 2 ( t ) = t 2 − 2 3 t − 2 1 implied that n ! ( n − 1 ) ! F n ( 0 ) = − 1 T r [ A ( n ) − 1 ] = n ! ( n − 1 ) ! F n ′ ( 0 ) = 1 − 3 2 ( n − 1 ) n ( n + 1 ) n ≥ 1 Following these ideas further, the fact that the determinant or a matrix is a multilinear function of its rows means that d e t B n − 1 ( t ) = ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ − 1 − 2 1 t I n − 2 − A ( n − 2 ) ⋯ − n − 2 1 − 1 − 2 1 ⋮ ⋮ − n − 2 1 − n − 1 1 ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ = ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ − 1 − 2 1 t I n − 2 − A ( n − 2 ) ⋯ − n − 2 1 − 1 − 2 1 ⋮ ⋮ − n − 2 1 t − n − 1 1 ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ − ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ 0 0 t I n − 2 − A ( n − 2 ) ⋯ 0 − 1 − 2 1 ⋮ ⋮ − n − 2 1 t ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ = F n − 1 ( t ) − t F n − 2 ( t ) for n ≥ 3 , so we deduce that F n ( t ) = ( 2 t + n ( n − 1 ) 1 ) F n − 1 ( t ) − t 2 F n − 2 ( t ) n ≥ 3 and hence that F n ′ ′ ( 0 ) = n ( n − 1 ) 1 F n − 1 ′ ′ ( 0 ) + 4 F n − 1 ′ ( 0 ) − 2 F n − 2 ( 0 ) n ≥ 3 This implies that n ! ( n − 1 ) ! F n ′ ′ ( 0 ) = ( n − 1 ) ! ( n − 2 ) ! F n − 1 ′ ′ ( 0 ) + 4 n ! ( n − 1 ) ! F n − 1 ′ ( 0 ) − 2 n ! ( n − 1 ) ! F n − 2 ( 0 ) = ( n − 1 ) ! ( n − 2 ) ! F n − 1 ′ ′ ( 0 ) + 4 n ( n − 1 ) [ 1 − 3 2 ( n − 2 ) ( n − 1 ) n ] + 2 n ( n − 1 ) 2 ( n − 2 ) = ( n − 1 ) ! ( n − 2 ) ! F n − 1 ′ ′ ( 0 ) − 3 2 n ( n − 1 ) [ 4 n 3 − 1 5 n 2 + 1 7 n − 1 2 ] for n ≥ 3 , and hence n ! ( n − 1 ) ! F n ′ ′ ( 0 ) = 4 − 3 2 r = 3 ∑ n r ( r − 1 ) [ 4 r 3 − 1 5 r 2 + 1 7 r − 1 2 ] = − 9 4 n 6 + 5 6 n 5 − 9 1 n 4 + 9 5 n 2 − 5 6 n − 4 n ≥ 2 Using Vieta's formulae, if u n 1 , u n 2 , … , u n n are the eigenvalues of A ( n ) , then T r [ A ( n ) − 1 ] = j ∑ u n j − 1 j < k ∑ u n j − 1 u n k − 1 = − F n ( 0 ) F n ′ ( 0 ) = 1 − 3 2 ( n − 1 ) n ( n + 1 ) = 2 F n ( 0 ) F n ′ ′ ( 0 ) = 9 2 n 6 − 5 3 n 5 + 1 8 1 n 4 − 1 8 5 n 2 + 5 3 n + 2 for n ≥ 2 , from which we deduce that T r [ A ( n ) − 2 ] = j ∑ u n j − 2 = ( j ∑ u n j − 1 ) 2 − 2 j < k ∑ u n j − 1 u n k − 1 = 1 5 1 ( n − 1 ) n ( n + 1 ) ( 1 8 n 2 − 1 5 n − 2 ) − 3 for all n ≥ 2 . But then we deduce that X ( n ) = { 5 6 ( n − 2 ) ( n − 1 ) n ( n + 1 ) ( n + 2 ) 4 n ≥ 2 n = 1 and hence we deduce that n = 3 ∑ ∞ X ( n ) − 1 = 2 4 5 n = 3 ∑ ∞ ( ( n − 2 ) ( n − 1 ) n ( n + 1 ) 1 − ( n − 1 ) n ( n + 1 ) ( n + 2 ) 1 ) = 5 7 6 5 making the answer 5 + 5 7 6 = 5 8 1 .
Just as there was an alternate approach to the first part of this question, there is an alternate approach to the second part. Recall that
A ( n + 1 ) − 1 = ⎝ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎛ 0 0 A ( n ) − 1 − n ( n + 1 ) K ( n ) ⋯ 0 n ( n + 1 ) 0 0 ⋮ 0 n ( n + 1 ) − n ( n + 1 ) ⎠ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎞ where K ( n ) is the n × n matrix with zeros in all components except the bottom right one, which has the value 1 . This implies that A ( n + 1 ) − 2 = ⎝ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎛ ⋆ ⋆ ( A ( n ) − 1 − n ( n + 1 ) K ( n ) ) 2 + n 2 ( n + 1 ) 2 K ( n ) ⋯ ⋆ ⋆ ⋆ ⋆ ⋮ ⋆ ⋆ 2 n 2 ( n + 1 ) 2 ⎠ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎞ where the stars indicate that the values in those places are unimportant. Thus T r [ A ( n + 1 ) − 2 ] = T r [ ( A ( n ) − 1 − n ( n + 1 ) K ( n ) ) 2 ] + 3 n 2 ( n + 1 ) 2 = T r [ A ( n ) − 2 ] − 2 n ( n + 1 ) T r [ K ( n ) A ( n ) − 1 ] + 4 n 2 ( n + 1 ) 2
It is easy to calculate that T r [ K ( n + 1 ) A ( n + 1 ) − 1 ] = − n ( n + 1 ) for all n ≥ 1 , and hence T r [ A ( n + 1 ) − 1 ] = T r [ A ( n ) − 2 ] + 2 ( n + 1 ) n 2 ( n − 1 ) + 4 n 2 ( n + 1 ) 2 = T r [ A ( n ) − 2 ] + 2 n 2 ( n + 1 ) ( 3 n + 1 ) for all n ≥ 2 , and hence T r [ A ( n ) − 2 ] = 1 3 + 2 r = 2 ∑ n − 1 r 2 ( r + 1 ) ( 3 r + 1 ) = 1 5 1 ( n − 1 ) n ( n + 1 ) ( 1 8 n 2 − 1 5 n − 2 ) − 3 for n ≥ 2 , as before.
Simplest yet, the above observations about the matrix A ( n ) make it easy to show that the inverse of A ( n ) is the symmetric tridiagonal matrix A ( n ) − 1 = ⎝ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎛ a 1 b 1 0 0 ⋮ 0 0 b 1 a 2 b 2 0 ⋮ 0 0 0 b 2 a 3 b 3 ⋮ 0 0 0 0 b 3 a 4 ⋮ 0 0 ⋯ ⋯ ⋯ ⋯ ⋱ ⋯ ⋯ 0 0 0 0 ⋮ a n − 1 b n − 1 0 0 0 0 ⋮ b n − 1 a n ⎠ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎞ where a j = ⎩ ⎨ ⎧ − 1 − 2 j 2 − n ( n − 1 ) j = 1 2 ≤ j ≤ n − 1 j = n b j = j ( j + 1 ) 1 ≤ j ≤ n − 1 and hence we can just calculate T r [ A ( n ) − 1 ] = j = 1 ∑ n a j T r [ A ( n ) − 2 ] = j = 1 ∑ n a j 2 + 2 j = 1 ∑ n − 1 b j 2