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Hello all,

Parallel implementation of Jacobi with relaxation Linear Algebraic System

Solver version 1.0

Description:

The Parallel iterative with relaxation method that i programmed here is

designed to be used to solve large sparse systems of linear equations where

the direct methods can exceed available machine memory and/or be extremely

time-consuming. for example the direct method of the Gauss algorithm takes

O(n^2) in the back substitution process and is dominated by the O(n^3)

forward elimination process, that means, if for example an operation takes

10^-9 second and we have 1000 equations , the elimination process in the

Jacobi algorithm will takes 0.7 second, but if we have 10000 equations in

the system , the elimination process in the Gauss algorithm will take 11

minutes !. This is why i have develloped for you the Parallel Jacobi with

relaxation iterative algorithm in Object Pascal, that is very fast.

Please read more here:

http://pages.videotron.com/aminer/ParallelJacobiWithRelaxation/pjr.htm

Please look at test.pas example inside the zip file, compile and execute

it...

You can download Parallel implementation of Jacobi with relaxation Linear

Algebraic System Solver version 1.0 from:

http://pages.videotron.com/aminer/

Language: FPC Pascal v2.2.0+ / Delphi 7+: http://www.freepascal.org/

Operating Systems: Win , Linux and Mac (x86).

Required FPC switches: -O3 -Sd -dFPC -dWin32 -dFreePascal

-Sd for delphi mode....

-dUnix for Linux,MacOSX etc.

Required Delphi switches: -DMSWINDOWS -$H+ -DDelphi

Thank you.

Amine Moulay Ramdane.

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2 Replies

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Hello,

If you want tocompile Parallel implementation of Jacobi with relaxation

Linear System Solver version 1.0to theWin64 system, just set the CPU=64 in

the defines.inc.

In my Parallel algorithm i use a factor 'lambda' that is assigned a value

between 0 and 2 , if this factor is set between 0 and 1 we call it underrelaxation

and is typically employed to make nonconvergent system converge or to hasten

convergence by dampening out oscillations.

In my Parallel algorithm i use a factor 'lambda' that is assigned a value

between 0 and 2 , if this factor is set between 0 and 1 we call it underrelaxation

and is typically employed to make nonconvergent system converge or to hasten

convergence by dampening out oscillations.

If the value of lambda is comprised between 1 and 2, we call it overrelaxation,

and is designed to 'accelerate' the convergence of an already convergent system.

The approach is also called successive or simultaneous overrelaxation or SOR.

And a theorem due to Kahan shows that SOR fails to converge if it is outside the

interval [0,2] .

Now i have played with the lambda and i have noticed that when you set size

and is designed to 'accelerate' the convergence of an already convergent system.

The approach is also called successive or simultaneous overrelaxation or SOR.

And a theorem due to Kahan shows that SOR fails to converge if it is outside the

interval [0,2] .

Now i have played with the lambda and i have noticed that when you set size

of the matrix to a number, you have to count the number of digits in this number,

for example if the size of the matrix is 2000 , it means the size is 4 digits. Now

i have noticed that to make the system converge you have to set the lambda

to the same number of digits than the size or set it to thenumber of digits than

the size of the matrix+ 1.

Example if the size is 2000you have to set the the number of digits before the comma

and after the comma to the same number of digits than the size or set it to the number of digits

of the size of the matrix +1 , that means that in this exampleto make the system

converge set the lambda to 0,001 or 0,0001.

and if the size of the matrix is 3 , set the lambda to 1 or 0,1 to make the systemconverge.

Thank you.

Amine Moulay Ramdane.

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Hello,

Red-black Gauss-Seidel converges twice as fast as Jacobi,

but there are twice as many parallel steps, so the same in practice

Thank you.

Amine Moulay Ramdane.

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