# Gaussian Elimination - Numerical Analysis - Solved Exam, Exams for Mathematical Methods for Numerical Analysis and Optimization. Central University of Kerala

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Main Points are:Gaussian Elimination, Set of Equations, Simultaneous Linear Equations, Back Substitution, Velocity Data, Naïve Gauss Elimination Method, Solution Vector, Matrix Multiplication, Values of Velocity
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04.06.1

Chapter 04.06 Gaussian Elimination After reading this chapter, you should be able to:

1. solve a set of simultaneous linear equations using Naïve Gauss elimination, 2. learn the pitfalls of the Naïve Gauss elimination method, 3. understand the effect of round-off error when solving a set of linear equations with

the Naïve Gauss elimination method, 4. learn how to modify the Naïve Gauss elimination method to the Gaussian elimination

with partial pivoting method to avoid pitfalls of the former method, 5. find the determinant of a square matrix using Gaussian elimination, and 6. understand the relationship between the determinant of a coefficient matrix and the

solution of simultaneous linear equations. How is a set of equations solved numerically? One of the most popular techniques for solving simultaneous linear equations is the Gaussian elimination method. The approach is designed to solve a general set of n equations and n unknowns

11313212111 ... bxaxaxaxa nn =++++

22323222121 ... bxaxaxaxa nn =++++ . . . . . .

nnnnnnn bxaxaxaxa =++++ ...332211 Gaussian elimination consists of two steps

1. Forward Elimination of Unknowns: In this step, the unknown is eliminated in each equation starting with the first equation. This way, the equations are reduced to one equation and one unknown in each equation.

2. Back Substitution: In this step, starting from the last equation, each of the unknowns is found.

Forward Elimination of Unknowns:

In the first step of forward elimination, the first unknown, 1x is eliminated from all rows below the first row. The first equation is selected as the pivot equation to eliminate 1x . So,

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04.06.2 Chapter 04.06

to eliminate 1x in the second equation, one divides the first equation by 11a (hence called the pivot element) and then multiplies it by 21a . This is the same as multiplying the first equation by 1121 / aa to give

1 11

21 1

11

21 212

11

21 121 ... ba

axa a axa

a axa nn =+++

Now, this equation can be subtracted from the second equation to give

1 11

21 21

11

21 2212

11

21 22 ... ba

abxa a aaxa

a aa nnn −=

  

 −++

  

 −

or 22222 ... bxaxa nn ′=′++′

where

nnn aa aaa

a a aaa

1 11

21 22

12 11

21 2222

−=′

−=′

This procedure of eliminating 1x , is now repeated for the third equation to the thn equation

to reduce the set of equations as 11313212111 ... bxaxaxaxa nn =++++

22323222 ... bxaxaxa nn ′=′++′+′

33333232 ... bxaxaxa nn ′=′++′+′ . . . . . . . . .

nnnnnn bxaxaxa ′=′++′+′ ...3322 This is the end of the first step of forward elimination. Now for the second step of forward elimination, we start with the second equation as the pivot equation and 22a′ as the pivot element. So, to eliminate 2x in the third equation, one divides the second equation by 22a′ (the pivot element) and then multiply it by 32a′ . This is the same as multiplying the second equation by 2232 / aa ′′ and subtracting it from the third equation. This makes the coefficient of

2x zero in the third equation. The same procedure is now repeated for the fourth equation till the thn equation to give

11313212111 ... bxaxaxaxa nn =++++

22323222 ... bxaxaxa nn ′=′++′+′

33333 ... bxaxa nn ′′=′′++′′ . . . . . .

nnnnn bxaxa ′′=′′++′′ ...33

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Gaussian Elimination 04.06.3

The next steps of forward elimination are conducted by using the third equation as a pivot equation and so on. That is, there will be a total of 1−n steps of forward elimination. At the end of 1−n steps of forward elimination, we get a set of equations that look like

++ 212111 xaxa 11313 ... bxaxa nn =++ 22323222 ... bxaxaxa nn ′=′++′+′ 33333 ... bxaxa nn ′′=′′++′′ . . . . . . ( ) ( )11 −− = nnn

n nn bxa

Back Substitution: Now the equations are solved starting from the last equation as it has only one unknown.

)1(

)1(

= n nn

n n

n a b

x

Then the second last equation, that is the th)1( −n equation, has two unknowns: nx and 1−nx , but nx is already known. This reduces the

th)1( −n equation also to one unknown. Back substitution hence can be represented for all equations by the formula

( ) ( )

( )1 1

11

− +=

−− ∑− = i

ii

n

ij j

i ij

i i

i a

xab x for 1,,2,1 −−= nni

and

)1(

)1(

= n nn

n n

n a b

x

Example 1 The upward velocity of a rocket is given at three different times in Table 1.

Table 1 Velocity vs. time data.

Time, t (s) Velocity, v (m/s)

5 106.8 8 177.2 12 279.2

The velocity data is approximated by a polynomial as

( ) 125 , 3221 ≤≤++= tatatatv The coefficients 321 and a, , aa for the above expression are given by

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04.06.4 Chapter 04.06

  

  

 =   

  

  

  

2.279 2.177 8.106

112144 1864 1525

3

2

1

a a a

Find the values of 321 and a,, aa using the Naïve Gauss elimination method. Find the velocity at 11 ,9 ,5.7 ,6=t seconds. Solution Forward Elimination of Unknowns Since there are three equations, there will be two steps of forward elimination of unknowns. First step Divide Row 1 by 25 and then multiply it by 64, that is, multiply Row 1 by 2.5664/25 = .

[ ] [ ]( ) 56.28.106 1525 × gives Row 1 as [ ] [ ]408.27356.28.1264

Subtract the result from Row 2

[ ] [ ] [ ] [ ]

208.9656.18.40 408.27356.28.1264 2.1771 864

−−− −

to get the resulting equations as

  

  

 −=

  

  

  

  

 −−

2.279 208.96 8.106

112144 56.18.40

1525

3

2

1

a a a

Divide Row 1 by 25 and then multiply it by 144, that is, multiply Row 1 by 5.76144/25 = . [ ] [ ]( ) 76.58.106 1525 × gives Row 1 as [ ] [ ]168.61576.58.28144

Subtract the result from Row 3

[ ] [ ] [ ] [ ]

968.33576.48.16 0 168.61576.58.28144 2.2791 12144

−−− −

to get the resulting equations as

  

  

− −=

  

  

  

  

−− −−

968.335 208.96 8.106

76.48.160 56.18.40

1525

3

2

1

a a a

Second step We now divide Row 2 by –4.8 and then multiply by –16.8, that is, multiply Row 2 by

3.54.816.8/ =−− . [ ] [ ]( ) 5.3208.96 56.18.40 ×−−− gives Row 2 as [ ] [ ]728.33646.58.160 −−−

Subtract the result from Row 3

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Gaussian Elimination 04.06.5

[ ] [ ] [ ] [ ]

76.0 7.0 0 0 728.33646.58.160 968.3354.768.160

−−−− −−−

to get the resulting equations as

  

  

 −=

  

  

  

  

 −−

76.0 208.96 8.106

7.000 56.18.40

1525

3

2

1

a a a

Back substitution From the third equation

76.07.0 3 =a

70 760 3 . .a =

1.08571 = Substituting the value of 3a in the second equation,

208.9656.18.4 32 −=−− aa

8.4 56.1208.96 3

2 − +−

= a

a

4.8

08571.11.5696.208 −

×+− =

690519. = Substituting the value of 2a and 3a in the first equation,

8.106525 321 =++ aaa

25 58.106

321 aa

a −−

=

25

08571.16905.1958.106 −×−=

290472.0 = Hence the solution vector is

  

  

 =   

  

08571.1 6905.19

290472.0

3

2

1

a a a

The polynomial that passes through the three data points is then ( ) 3221 atatatv ++=

125 ,08571.16905.19290472.0 2 ≤≤++= ttt Since we want to find the velocity at 11 and 9 ,5.7 ,6=t seconds, we could simply substitute each value of t in ( ) 08571.16905.19290472.0 2 ++= tttv and find the corresponding velocity. For example, at 6=t

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04.06.6 Chapter 04.06

( ) ( ) ( ) m/s686.129

08571.166905.196290472.06 2

= ++=v

However we could also find all the needed values of velocity at t = 6, 7.5, 9, 11 seconds using matrix multiplication.

( ) [ ]   

  

= 1

0857116905192904720

2

t t

. . .tv

So if we want to find ( ) ( ) ( ) ( ),11 ,9 ,5.7 ,6 vvvv it is given by

( ) ( ) ( ) ( )[ ] [ ]   

  

= 1111 1195.76

1195.76 08571.1 6905.19 0.290472 11 9 5.7 6

2222

vvvv

[ ]   

  

 =

1111 1197.56

1218156.2536 1.08571 19.6905 290472.0

[ ]252.828 201.828 165.104 686.129= m/s 686.129)6( =v

m/s 041.165)5.7( =v m/s 828.201)9( =v m/s 828.252)11( =v

Example 2 Use Naïve Gauss elimination to solve

45101520 321 =++ xxx 751.17249.23 321 =+−− xxx

935 321 =++ xxx Use six significant digits with chopping in your calculations. Solution Working in the matrix form

  

  

 −−

315 7249.23

101520

  

  

3

2

1

x x x

=   

  

9 751.1 45

Forward Elimination of Unknowns

First step Divide Row 1 by 20 and then multiply it by –3, that is, multiply Row 1 by 15.020/3 −=− .

[ ] [ ]( ) 15.045101520 −× gives Row 1 as [ ] [ ]75.65.125.23 −−−−

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Gaussian Elimination 04.06.7

Subtract the result from Row 2

[ ] [ ] [ ] [ ]

501.8 5.8 001.0 0 75.65.125.23

751.17 249.23 −−−−−

−−

to get the resulting equations as

  

  

315 5.8001.00

101520

  

  

3

2

1

x x x

=   

  

9 501.8 45

Divide Row 1 by 20 and then multiply it by 5, that is, multiply Row 1 by 25.020/5 = [ ] [ ]( ) 25.045101520 × gives Row 1 as [ ] [ ]25.115.275.35

Subtract the result from Row 3

[ ] [ ] [ ] [ ]

2.25 .5075.20 25.115.2 75.3 5

93 1 5

−− −

to get the resulting equations as

  

  

− 5.075.20 5.8001.00

101520

  

  

3

2

1

x x x

=   

  

− 25.2 501.8 45

Second step Now for the second step of forward elimination, we will use Row 2 as the pivot equation and eliminate Row 3: Column 2. Divide Row 2 by 0.001 and then multiply it by –2.75, that is, multiply Row 2 by

2750001.0/75.2 −=− . [ ] [ ]( ) 2750501.85.8001.00 −× gives Row 2 as [ ] [ ]75.233772337575.20 −−−

Rewriting within 6 significant digits with chopping [ ] [ ]7.233772337575.20 −−−

Subtract the result from Row 3

[ ] [ ] [ ] [ ]

3375.452 5.23375 0 0 7.2337723375 2.75 0

25.2.50 75.2 0 −−−− −−

Rewriting within 6 significant digits with chopping [ ] [ ]4.233755.2337500 − to get the resulting equations as

  

  

5.2337500 5.8001.00

101520   

  

3

2

1

x x x

=   

  

4.23375 501.8 45

This is the end of the forward elimination steps.

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04.06.8 Chapter 04.06

Back substitution We can now solve the above equations by back substitution. From the third equation,

4.233755.23375 3 =x

5.23375 4.23375

3 =x

999995.0= Substituting the value of 3x in the second equation

501.85.8001.0 32 =+ xx

0.001 0.999995585018

001.0 5.8501.8 3

2

×− =

− =

..

xx

001.0

49995.8501.8 − =

001.0

00105.0 =

05.1= Substituting the value of 3x and 2x in the first equation,

45101520 321 =++ xxx

20 10 1545 32

1 xx

x −−

=

20

999995.01005.11545 ×−×−=

20 2500.19

20 99995.925.29

20 99995.975.1545

=

− =

−− =

9625.0 = Hence the solution is

  

  

 =

3

2

1

][ x x x

X

  

  

 =

999995.0 05.1

9625.0

Compare this with the exact solution of

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Gaussian Elimination 04.06.9

[ ]   

  

 =

3

2

1

x x x

X

  

  

 =

1 1 1

Are there any pitfalls of the Naïve Gauss elimination method? Yes, there are two pitfalls of the Naïve Gauss elimination method. Division by zero: It is possible for division by zero to occur during the beginning of the

1−n steps of forward elimination. For example

1165 32 =+ xx 16754 321 =++ xxx 15329 321 =++ xxx

will result in division by zero in the first step of forward elimination as the coefficient of 1x in the first equation is zero as is evident when we write the equations in matrix form.

  

  

 =   

  

  

  

15 16 11

329 754 650

3

2

1

x x x

But what about the equations below: Is division by zero a problem? 18765 321 =++ xxx

2531210 321 =++ xxx 56191720 321 =++ xxx

Written in matrix form,

  

  

 =   

  

  

  

56 25 18

191720 31210 765

3

2

1

x x x

there is no issue of division by zero in the first step of forward elimination. The pivot element is the coefficient of 1x in the first equation, 5, and that is a non-zero number. However, at the end of the first step of forward elimination, we get the following equations in matrix form

  

  

− −=

  

  

  

  

−− −

16 11

18

970 1100 765

3

2

1

x x x

Now at the beginning of the 2nd step of forward elimination, the coefficient of 2x in Equation 2 would be used as the pivot element. That element is zero and hence would create the division by zero problem.

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04.06.10 Chapter 04.06

So it is important to consider that the possibility of division by zero can occur at the beginning of any step of forward elimination. Round-off error: The Naïve Gauss elimination method is prone to round-off errors. This is true when there are large numbers of equations as errors propagate. Also, if there is subtraction of numbers from each other, it may create large errors. See the example below. Example 3 Remember Example 2 where we used Naïve Gauss elimination to solve

45101520 321 =++ xxx 751.17249.23 321 =+−− xxx

935 321 =++ xxx using six significant digits with chopping in your calculations? Repeat the problem, but now use five significant digits with chopping in your calculations. Solution Writing in the matrix form

  

  

 −−

315 7249.23

101520   

  

3

2

1

x x x

=   

  

9 751.1 45

Forward Elimination of Unknowns

First step Divide Row 1 by 20 and then multiply it by –3, that is, multiply Row 1 by 15.020/3 −=− .

[ ] [ ]( ) 15.045101520 −× gives Row 1 as [ ] [ ]75.65.125.23 −−−−

Subtract the result from Row 2

[ ] [ ] [ ] [ ]

501.8 5.8 001.0 0 75.65.125.23

751.17 249.23 −−−−−

−−

to get the resulting equations as

  

  

315 5.8001.00

101520

  

  

3

2

1

x x x

=   

  

9 501.8 45

Divide Row 1 by 20 and then multiply it by 5, that is, multiply Row 1 by 25.020/5 = . [ ] [ ]( ) 25.045101520 × gives Row 1 as [ ] [ ]25.115.275.35

Subtract the result from Row 3

[ ] [ ] [ ] [ ]

2.25 .5075.20 25.115.2 75.3 5

93 1 5

−− −

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Gaussian Elimination 04.06.11

to get the resulting equations as

  

  

− 5.075.20 5.8001.00

101520

  

  

3

2

1

x x x

=   

  

− 25.2 501.8 45

Second step Now for the second step of forward elimination, we will use Row 2 as the pivot equation and eliminate Row 3: Column 2. Divide Row 2 by 0.001 and then multiply it by –2.75, that is, multiply Row 2 by

2750001.0/75.2 −=− . [ ] [ ]( ) 2750501.85.8001.00 −× gives Row 2 as [ ] [ ]75.233772337575.20 −−−

Rewriting within 5 significant digits with chopping [ ] [ ]233772337575.20 −−−

Subtract the result from Row 3

[ ] [ ] [ ] [ ]

33742 23375 0 0 2337723375 2.75 0

25.2.50 75.2 0 −−−− −−

Rewriting within 6 significant digits with chopping [ ] [ ]233742337500 − to get the resulting equations as

  

  

2337500 5.8001.00

101520   

  

3

2

1

x x x

=   

  

23374 501.8 45

This is the end of the forward elimination steps. Back substitution We can now solve the above equations by back substitution. From the third equation,

2337423375 3 =x

23375 23374

3 =x

99995.0 = Substituting the value of 3x in the second equation

501.85.8001.0 32 =+ xx

0.001 0.99995585018

001.0 5.8501.8 3

2

×− =

− =

..

xx

001.0 4995.8501.8

001.0 499575.8501.8

− =

− =

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04.06.12 Chapter 04.06

001.0

0015.0 =

5.1= Substituting the value of 3x and 2x in the first equation,

45101520 321 =++ xxx

20 10 1545 32

1 xx

x −−

=

20

99995.0105.11545 ×−×−=

20 500.12 20 5005.12

20 9995.95.22 20

9995.95.2245

=

=

− =

−− =

625.0 = Hence the solution is

[ ]   

  

 =

3

2

1

x x x

X

  

  

 =

99995.0 5.1

625.0

Compare this with the exact solution of

[ ]   

  

 =

3

2

1

x x x

X   

  

 =

1 1 1

What are some techniques for improving the Naïve Gauss elimination method? As seen in Example 3, round off errors were large when five significant digits were used as opposed to six significant digits. One method of decreasing the round-off error would be to use more significant digits, that is, use double or quad precision for representing the numbers. However, this would not avoid possible division by zero errors in the Naïve Gauss elimination method. To avoid division by zero as well as reduce (not eliminate) round-off error, Gaussian elimination with partial pivoting is the method of choice.

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Gaussian Elimination 04.06.13

How does Gaussian elimination with partial pivoting differ from Naïve Gauss elimination? The two methods are the same, except in the beginning of each step of forward elimination, a row switching is done based on the following criterion. If there are n equations, then there are 1−n forward elimination steps. At the beginning of the thk step of forward elimination, one finds the maximum of

kka , kka ,1+ , …………, nka

Then if the maximum of these values is pka in the thp row, npk ≤≤ , then switch rows p

and k . The other steps of forward elimination are the same as the Naïve Gauss elimination method. The back substitution steps stay exactly the same as the Naïve Gauss elimination method. Example 4 In the previous two examples, we used Naïve Gauss elimination to solve

45101520 321 =++ xxx 751.17249.23 321 =+−− xxx

935 321 =++ xxx using five and six significant digits with chopping in the calculations. Using five significant digits with chopping, the solution found was

[ ]   

  

 =

3

2

1

x x x

X

  

  

 =

99995.0 5.1

625.0

This is different from the exact solution of

[ ]   

  

 =

3

2

1

x x x

X

  

  

 =

1 1 1

Find the solution using Gaussian elimination with partial pivoting using five significant digits with chopping in your calculations. Solution

  

  

 −−

315 7249.23

101520   

  

3

2

1

x x x

=   

  

9 751.1 45

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04.06.14 Chapter 04.06

Forward Elimination of Unknowns Now for the first step of forward elimination, the absolute value of the first column elements below Row 1 is

20 , 3− , 5 or

20, 3, 5 So the largest absolute value is in the Row 1. So as per Gaussian elimination with partial pivoting, the switch is between Row 1 and Row 1 to give

  

  

 −−

315 7249.23

101520   

  

3

2

1

x x x

=   

  

9 751.1 45

Divide Row 1 by 20 and then multiply it by –3, that is, multiply Row 1 by 15.020/3 −=− . [ ] [ ]( ) 15.045101520 −× gives Row 1 as [ ] [ ]75.65.125.23 −−−−

Subtract the result from Row 2

[ ] [ ] [ ] [ ]

501.8 5.8 001.0 0 75.65.125.23

751.17 249.23 −−−−−

−−

to get the resulting equations as

  

  

315 5.8001.00

101520

  

  

3

2

1

x x x

=   

  

9 501.8 45

Divide Row 1 by 20 and then multiply it by 5, that is, multiply Row 1 by 25.020/5 = . [ ] [ ]( ) 25.045101520 × gives Row 1 as [ ] [ ]25.115.275.35

Subtract the result from Row 3

[ ] [ ] [ ] [ ]

2.25 .5075.20 25.115.2 75.3 5

93 1 5

−− −

to get the resulting equations as

  

  

− 5.075.20 5.8001.00

101520

  

  

3

2

1

x x x

=

  

  

− 25.2 501.8 45

This is the end of the first step of forward elimination. Now for the second step of forward elimination, the absolute value of the second column elements below Row 1 is

001.0 , 75.2− or

0.001, 2.75

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Gaussian Elimination 04.06.15

So the largest absolute value is in Row 3. So Row 2 is switched with Row 3 to give

  

  

 −

5.8001.00 5.075.20

101520

  

  

3

2

1

x x x

=   

  

 −

501.8 25.2

7

Divide Row 2 by –2.75 and then multiply it by 0.001, that is, multiply Row 2 by 00036363.075.2/001.0 −=− .

[ ] [ ]( ) 00036363.025.25.075.20 −×−− gives Row 2 as [ ] [ ]00081816.000018182.000099998.00 −

Subtract the result from Row 3

[ ] [ ] [ ] [ ]

.500181848 50018182.8 0 0 00081816.00.00018182 .000999980 0 501.8.58 .0010 0

−−

Rewriting within 5 significant digits with chopping [ ] [ ]5001.85001.800

to get the resulting equations as

  

  

 −

5001.800 5.075.20

101520

  

  

3

2

1

x x x

=   

  

 −

5001.8 25.2

45

Back substitution

5001.85001.8 3 =x

5001.8 5001.8

3 =x

=1 Substituting the value of 3x in Row 2

25.25.075.2 32 −=+− xx

75.2 5.025.2 2

2 − −−

= xx

75.2

15.025.2 −

×−− =

75.2

5.025.2 − −−

=

75.2 75.2

− − =

1 = Substituting the value of 3x and 2x in Row 1

45101520 321 =++ xxx

20 101545 32

1 xx

x −−

=

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04.06.16 Chapter 04.06

20

11011545 ×−×− =

20 1030

20 101545

− =

−− =

20 20 =

1= So the solution is

[ ]   

  

 =

3

2

1

x x x

X

=   

  

1 1 1

This, in fact, is the exact solution. By coincidence only, in this case, the round-off error is fully removed. Can we use Naïve Gauss elimination methods to find the determinant of a square matrix? One of the more efficient ways to find the determinant of a square matrix is by taking advantage of the following two theorems on a determinant of matrices coupled with Naïve Gauss elimination. Theorem 1:

Let ][A be a nn× matrix. Then, if ][B is a nn× matrix that results from adding or subtracting a multiple of one row to another row, then )det()det( BA = (The same is true for column operations also). Theorem 2:

Let ][A be a nn× matrix that is upper triangular, lower triangular or diagonal, then

nnii aaaaA ×××××= ......)det( 2211

∏ =

= n

i iia

1

This implies that if we apply the forward elimination steps of the Naïve Gauss elimination method, the determinant of the matrix stays the same according to Theorem 1. Then since at the end of the forward elimination steps, the resulting matrix is upper triangular, the determinant will be given by Theorem 2.

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Gaussian Elimination 04.06.17

Example 5 Find the determinant of

  

  

 =

112144 1864 1525

][A

Solution Remember in Example 1, we conducted the steps of forward elimination of unknowns using the Naïve Gauss elimination method on ][A to give

[ ]   

  

 −−=

7.000 56.18.40

1525 B

According to Theorem 2 )det()det( BA =

7.0)8.4(25 ×−×= 00.84−=

What if I cannot find the determinant of the matrix using the Naïve Gauss elimination method, for example, if I get division by zero problems during the Naïve Gauss elimination method? Well, you can apply Gaussian elimination with partial pivoting. However, the determinant of the resulting upper triangular matrix may differ by a sign. The following theorem applies in addition to the previous two to find the determinant of a square matrix. Theorem 3:

Let ][A be a nn× matrix. Then, if ][B is a matrix that results from switching one row with another row, then )det()det( AB −= . Example 6 Find the determinant of

  

  

− −

− =

515 6099.23 0710

][A

Solution The end of the forward elimination steps of Gaussian elimination with partial pivoting, we would obtain

  

  

 − =

002.600 55.20 0710

][B

( ) 002.65.210det ××=B

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04.06.18 Chapter 04.06

05.150= Since rows were switched once during the forward elimination steps of Gaussian elimination with partial pivoting,

( ) )det(det BA −= 05.150−= Example 7 Prove

( )1det 1)det( −= A

A

Solution

( ) ( ) ( ) ( ) ( ) ( )1

1

1

1

det 1det

1detdet det det

][]][[

=

=

=

=

A A

AA IAA

IAA

If ][A is a nn× matrix and 0)det( ≠A , what other statements are equivalent to it? 1. ][A is invertible. 2. 1][ −A exists. 3. ][][][ CXA = has a unique solution. 4. ]0[][][ =XA solution is ]0[][

=X . 5. ][][][][][ 11 AAIAA −− == .

Key Terms: Naïve Gauss Elimination Partial Pivoting Determinant

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