HP 15c User Manual

Page 90

Advertising
background image

90

Section 4: Using Matrix Operations

90

Multiplying the calculated inverse and the original matrix verifies that the calculated inverse
is poor.

The trouble is that E is badly scaled. A well-scaled matrix, like A, has all its rows and
columns comparable in norm and the same must hold true for its inverse. The rows and
columns of E are about as comparable in norm as those of A, but the first row and column of
E

−1

are small in norm compared with the others. Therefore, to achieve better numerical

results, the rows and columns of E should be scaled before the matrix is inverted. This means
that the diagonal matrices L and R discussed earlier should be chosen to make LER and
(LER)

−1

= R

−1

E

−1

L

−1

not so badly scaled.

In general, you can't know the true inverse of matrix E in advance. So the detection of bad
scaling in E and the choice of scaling matrices L and R must be based on E and the
calculated E

−1

. The calculated E

−1

shows poor scaling and might suggest trying

5

5

5

10

0

0

0

10

0

0

0

10

R

L

.

Using these scaling matrices,

 

30

30

30

30

10

10

10

2

10

10

1

2

1

10

3

LER

,

which is still poorly scaled, but not so poorly that the HP-15C can’t cope. The calculated
inverse is

30

30

30

30

30

1

10

10

2

1

10

2

10

4

3

1

3

10

2

)

(LER

.

This result is correct to 10 digits, although you wouldn't be expected to know this. This result
is verifiably correct in the sense that using the calculated inverse,

(LER)

−1

(LER) = (LER)(LER)

−1

= I (the identity matrix)

to 10 digits.

Then E

−1

is calculated as

40

40

40

40

40

1

1

10

10

2

1

10

2

10

4

3

1

3

10

2

)

(

L

LER

R

E

,

which is correct to 10 digits.

Advertising