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```Advances in Linear Algebra & Matrix Theory, 2015, 5, 150-155
Published Online December 2015 in SciRes. http://www.scirp.org/journal/alamt
http://dx.doi.org/10.4236/alamt.2015.54015
Trace of Positive Integer Power of Real
2 × 2 Matrices
Department of Mathematics, Maulana Azad National Institute of Technology, Bhopal, India
Received 21 September 2015; accepted 4 December 2015; published 7 December 2015
Abstract
The purpose of this paper is to discuss the theorems for the trace of any positive integer power of
2 × 2 real matrix. We obtain a new formula to compute trace of any positive integer power of 2 × 2
real matrix A, in the terms of Trace of A (TrA) and Determinant of A (DetA), which are based on
definition of trace of matrix and multiplication of the matrixn times, where n is positive integer
and this formula gives some corollary for TrAn when TrA or DetA are zero.
Keywords
Trace, Determinant, Matrix Multiplication
1. Introduction
Traces of powers of matrices arise in several fields of mathematics, more specifically, Network Analysis, Numbertheory, Dynamical systems, Matrix theory, and Differential equations . When analyzing a complex network, an important problem is to compute the total number of triangles of a connected simple graph. This number is equal to Tr(A3)/6, where A is the adjacency matrix of the graph . Traces of powers of integer matrices
are connected with the Euler congruence , an important phenomenon in mathematics, stating that
( ) ≡ Tr ( A ) ( mod p ) ,
Tr A p
r
p r −1
r
for all integer matrices A, all primes p, and all r ∊ Z. The invariants of dynamical systems are described in terms
of the traces of powers of integer matrices, for example in studying the Lefschetz numbers . There are many
applications in matrix theory and numerical linear algebra. For example, in order to obtain approximations of the
*
Corresponding author.
How to cite this paper: Pahade, J. and Jha, M. (2015) Trace of Positive Integer Power of Real 2 × 2 Matrices. Advances in
Linear Algebra & Matrix Theory, 5, 150-155. http://dx.doi.org/10.4236/alamt.2015.54015
smallest and the largest eigenvalues of a symmetric matrix A, a procedure based on estimates of the trace of An
and A−n, n ∊ Z, is proposed in .
Trace of a n × n matrix A =  aij  is defined to be the sum of the elements on the main diagonal of A, i.e.
TrA = a11 + a22 +  + ann = ∑ i =1 aii
n
The computation of the trace of matrix powers has received much attention. In , an algorithm for computing Tr Ak , k ∈ Z is proposed, when A is a lower Hessenberg matrix with a unit codiagonal. In , a symbolic calculation of the trace of powers of tridiagonal matrices is presented. Let A be a symmetric positive definite matrix, and let {λk } denote its eigenvalues. For q ∊ R, Aq is also symmetric positive definite, and it holds
.
(1.1)
TrAq = ∑ k λkq
( )
This formula is restricted to the matrix A. Also we have other formulae  to compute the trace of matrix
power such that
(1.2)
TrAn = ∑ k λkn
But for many cases, this formula is time consuming. For example
3±i 7
1 −1
and let we are to find TrA5. Eigenvalues of A are
, then by (1.2),
Consider a matrix A = 

2
2 2 
5
5
 3+i 7   3−i 7 
=
TrA 
 + 
 .
 2   2 
Computation of this value is time consuming. Therefore, other formulae to compute trace of matrix power are
needed. Now we give new theorems and corollaries to compute trace of matrix power. Our estimation for the
trace of An is based on the multiplication of matrix.
5
2. Main Result
Theorem 1. For even positive integer n and 2 × 2 real matrix A,
=
TrA
n
n 2
∑
r =0
( −1)
r!
r
n  n − ( r + 1)   n − ( r + 2 )  [ up to r terms ] ⋅ ( DetA ) ( TrA )
r
n−2r
a b 
Proof. Consider a matrix A = 
 where a, b, c, d are real.
c d 
Then
TrA= a + d
(2.1)
and
Det=
(2.2)
Now
2
 a b   a b   a + bc b ( a + d ) 
=
A2 = 

 
2 
 c d   c d  c ( a + d ) bc + d 
Then
TrA2 = a 2 + 2bc + d 2
= a 2 + 2ad + d 2 − 2ad + 2bc
=
=
( a + d ) − 2 ( ad − bc )
2
( TrA ) − 2DetA
2
151
(2.3)
Now again
 a 2 + bc b ( a + d )   a b 
A3 = A2 A = 

2 
c ( a + d ) bc + d   c d 
 a 3 + abc + bc ( a + d )
A3 = 
2
2
 ac ( a + d ) + bc + cd
a 2 b + b 2 c + bd ( a + d ) 

bc ( a + d ) + bcd + d 3 
(2.4)
Then
TrA3 = a 3 + d 3 + 3bc ( a + d )
= a 3 + d 3 + 3ad ( a + d ) − 3ad ( a + d ) + 3bc ( a + d )
=( a + d ) − 3 ( a + d )( ad − bc )
3
= ( TrA ) − 3 ( DetA )( TrA )
3
=
TrA3
( TrA )
− 3 ( DetA )( TrA )
3
(2.5)
2
Now replace A by A in (2.3), we have
2
=
TrA4 TrA2  − 2DetA2
2
2
2
= ( TrA ) – 2DetA − 2 ( DetA )  DetAB = ( DetA )( DetB ) 


=
TrA4
( TrA )
4
– 4DetA ( TrA ) + 2 ( DetA )
2
2
(2.6)
Again replace A by A2 in (2.5), we have
(
3
TrA6 TrA2  − 3DetA2 TrA2
=
)
3
2
2
2
= ( TrA ) − 2DetA − 3 ( DetA ) ( TrA ) − 2DetA




6
2
2
3
= ( TrA ) − 6 ( TrA ) DetA ( TrA ) − 2DetA − 8 ( DetA )


− 3 ( DetA ) ( TrA ) + 6 ( DetA )
2
2
3
TrA6 =
( TrA ) − 6DetA ( TrA ) + 9 ( DetA ) ( TrA ) − 2 ( DetA )
6
4
2
2
3
(2.7)
2
Now again replace A by A in (2.6), we have
4
2
TrA2  − 4DetA2 TrA2  + 2  DetA2 
TrA8 =
2
4
2
2
2
2
4
= ( TrA ) − 2DetA − 4 ( DetA ) ( TrA ) − 2DetA + 2 ( DetA )




8
2
4
2
4
4
2
=
( TrA ) − 8 ( DetA )( TrA ) ( TrA ) + 4 ( DetA )  + 16 ( DetA ) + 24 ( TrA ) ( DetA )
2
4
2
2
4
− 4 ( DetA ) ( TrA ) − 4 ( TrA ) ( DetA ) + 4 ( DetA )  + 2 ( DetA )


TrA8 =
( TrA ) − 8 ( DetA )( TrA ) + 20 ( DetA ) ( TrA ) − 16 ( DetA ) ( TrA ) + 2 ( DetA )
8
6
2
4
3
2
4
(2.8)
Now we observe from (2.3), (2.6), (2.7) and (2.8) that
TrA2 =
TrA =
4
( −1)
0!
0
( −1)
0!
( DetA ) ( TrA )
0
0
( DetA ) ( TrA )
0
( −1)
1
4 − 2× 0
+
1!
( −1)
1
2 − 2× 0
+
1!
4 ( DetA ) ( TrA )
1
152
2 ( DetA ) ( TrA )
4 − 2×1
1
+
( −1)
2!
2
2 − 2×1
4 ( 4 − 3)( DetA ) ( TrA )
2
4 − 2× 2
TrA =
6
( −1)
TrA =
( DetA ) ( TrA )
0
0!
+
8
0
( −1)
0
0!
1!
( −1)
1
8 − 2×0
0
3
6 ( DetA ) ( TrA )
6 ( 6 − 4 ) (6 − 5) ( DetA ) ( TrA )
( DetA ) ( TrA )
( −1)
+
1
3
3!
( −1)
+
3
( −1)
1
6 − 2×0
+
8 ( DetA ) ( TrA )
8 ( 8 − 4 )( 8 − 5 )( DetA ) ( TrA )
8 − 2×3
3
3!
+
( −1)
2
6 ( 6 − 3)( DetA ) ( TrA )
2
2!
6 − 2× 2
6 − 2× 3
8 − 2×1
1
1!
6 − 2×1
+
( −1)
4
4!
+
( −1)
2
8 ( 8 − 3)( DetA ) ( TrA )
8 − 2× 2
2
2!
8 ( 6 − 5 )( 8 − 6 )( 8 − 7 )( DetA ) ( TrA )
8 − 2× 4
3
Continuing this process up to n terms we get
TrA
=
n
( −1)
0
0!
+ +
+ +
( DetA ) ( TrA )
0
( −1)
r
( −1)
1
+
1!
n ( DetA ) ( TrA )
1
n −2×1
+
( −1)
2
n ( n − 3)( DetA ) ( TrA )
2
2!
n  n − ( r + 1)   n − ( r + 2 )   ( up to r terms ) ⋅ ( DetA ) ( TrA )
r
r!
( −1)
n −2×0
n 2
n  n ( n 2 + 1)   n − ( n 2 + 2 )   ( up to n 2 terms ) ⋅ ( DetA )
n 2!
n −2×2
n −2×r
n 2
(2.9)
( TrA )
n −2×n 2
Finally from above, we get
n 2
∑
=
TrAn
( −1)
r =0
r
n  n − ( r + 1)   n − ( r + 2 )  [ up to r terms ] ⋅ ( DetA ) ( TrA )
r
r!
n −2 r
(2.10)
Hence the proof is completed.
Theorem 2. For odd positive integer n and 2 × 2 real matrix A,
=
TrAn
( n−1)
∑
r =0
2
( −1)
r!
r
n  n − ( r + 1)   n − ( r + 2 )  [ up to r terms ] ⋅ ( DetA ) ( TrA )
r
n−2 r
Proof. Consider a matrix A as in theorem 1, we have from (1.4) and (1.6).
 a 3 + abc + bc ( a + d )
3 2
=
A5 A=
A 
2
2
 ac ( a + d ) + bc + cd
(
a 2b + b 2 c + bd ( a + d )   a 2 + bc b ( a + d ) 


bc ( a + d ) + bcd + d 3  c ( a + d ) bc + d 2 
)
TrA5 = a 2 + bc  a 3 + abc + bc ( a + d )  + c ( a + d )  a 2 b + b 2 c + bd ( a + d ) 
(
)
+ b ( a + d )  ac ( a + d ) + bc 2 + cd 2  + bc + d 2 bc ( a + d ) + bcd + d 3 
(
= a + d + 2bc a + d
5
5
3
3
) + 5b c ( a + d ) + 2bc ( a + d ) ( a
2 2
2
)
+ d 2 + bc ( a + d )
3
= a 5 + d 5 + 5bc ( a + d ) − 10abcd ( a + d ) + 5b 2 c 2 ( a + d )
3
= a 5 + d 5 + 5ad ( a + d ) − 5a 2 d 2 ( a + d ) − 5ad (a + d )3 + 5a 2 d 2 ( a + d )
3
+ 5bc ( a + d ) − 10abcd ( a + d ) + 5b 2 c 2 ( a + d )
3
=( a + d ) − 5 ( ad − bc )( a + d ) + 5ad ( ad − bc ) ( a + d )
5
3
2
TrA5 =
( TrA ) − 5DetA ( TrA ) + 5 ( DetA ) ( TrA )
5
3
2
(2.11)
Now we observe from (2.5) and (2.11) that
=
TrA3
( −1)
0!
0
( DetA ) ( TrA )
0
153
3−2×0
( −1)
1
+
1!
3 ( DetA ) ( TrA )
1
3−2×1
=
TrA
5
( −1)
0!
0
( DetA ) ( TrA )
0
5−2×0
( −1)
1
+
1!
5 ( DetA ) ( TrA )
1
5−2×1
+
( −1)
2!
2
5 ( 5 − 3)( DetA ) ( TrA )
2
5−2×2
n
Now we continuing this as in Theorem 1, we get TrA same as Theorem 1. But here r varies up to
Hence the theorem follows.
n
Corollary 1: For any positive integer n and 2 × 2 real singular matrix A, TrAn = ( TrA ) .
Proof: For singular matrix A, DetA = 0. Hence proof follows from Theorem 1 and Theorem 2.
Corollary 2: For 2 × 2 real matrix A with TrA = 0.
n
2 ( −DetA )
1) TrA=
n2
( n − 1)
2.
when n is even and;
2) TrA = 0 when n is odd.
Proof. Proof follows from theorem 1 and theorem 2.
Corollary 3: For 2 × 2 real matrix A with TrA = 0 and DetA = 0.
TrAn = 0 where n is any positive integer.
Proof. Proof follows from Corollary 2.
1 −1
5
Example 1. Consider a matrix A = 
 and let we are to find TrA .
2 2 
Here DetA = 2 + 2 =
4 and TrA =1 + 2 =3 . then by Theorem 2, we have
n
TrA5 =
−57
( TrA ) − 5DetA ( TrA ) + 5 ( DetA ) ( TrA ) =
( 3) − 5 ( 4 )( 3) + 5 ( 4 ) ( 3) =
5
3
2
5
3
2
3 −5 
10
Example 2. Consider a matrix A = 
 and let we are to find TrA .
1
−
2


Here DetA = −6 + 5 = −1 and TrA = 3 − 2 = 1 . then by Theorem 1, we have
TrA10 =
( TrA ) − 10DetA ( TrA ) + 35 ( DetA ) ( TrA ) − 50 ( DetA ) ( TrA )
10
8
2
+ 25 ( DetA ) ( TrA ) − 2 ( DetA )
4
2
6
3
4
5
=1 + 10 + 35 + 50 + 25 + 2 =123.
 20 199 
2015
Example 3. Consider a matrix A = 
 and let we are to find TrA .
−
−
2
20


Here TrA = 0, DetA = −2 and n = 2015, which is odd, hence by corollary 2, we get TrA2015 = 0.
21 
 15
100
Example 4. Consider a matrix A = 
 and let we are to find TrA .
 −10 −14 
Here A is a singular matrix with Trace 1, and then by Corollary 1, we have
Tr=
A100
( TrA=
) (1=
)
100
100
1.
Conclusion and Future Work
After to discuss Theorems 1 and 2, Corollaries 1, 2 and 3, we are able to find trace of any integer power of a 2
× 2 real matrix. In future, we can be developed similar results for 3 × 3 real matrices.
Acknowledgements
We would like to hardly thankful with great attitude to Director, Maulana Azad National Institute of Technology,
Bhopal for financial support and we also thankful to HOD, Department of Mathematics of this institute for giving me opportunity to expose my research in scientific world.
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Pan, V. (1990) Estimating the Extremal Eigenvalues of a Symmetric Matrix. Computers & Mathematics with Applications, 20, 17-22.

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https://en.wikipedia.org/wiki/Trace_
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