Difference between revisions of "Scilab/C4/Optimization-Using-Karmarkar-Function/Gujarati"
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Jyotisolanki (Talk | contribs) (Created page with "{| Border=1 |'''Time''' |'''Narration''' |- | 00:01 |નમસ્તે મિત્રો, |- | 00:02 | '''Scilab''' ઉપયોગ કરીને '''Optimization of...") |
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| − | | '''Optimization''' | + | | '''Optimization''' વ્યાપક રૂપથી આપેલ એન્જીનિયરિંગ અને નોન-એન્જીનિયરિંગ ક્ષેત્રો માં વધુ ઉપયોગ થાય છે. |
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| − | | | + | | ઇકોનોમિક્સ |
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| − | | | + | | કન્ટ્રોલ થીયરી અને |
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| − | | | + | | ઓપરેશન્સ અને રિસર્ચ. |
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| − | | | + | | '''Scilab function karmarkar''' આપેલમાં ઉપયોગ થાય છે: |
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| − | | | + | |લીન્યર ઓબ્જેક્ટીવ ફંકશન ને ઓપ્ટીમાઈઝ કરવા માટે, |
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| − | | | + | | લીનીયર '''constraints ''' પર |
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| − | || | + | || ડીસીજ્ન વેરીએબલસ પર. |
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| − | || | + | || આપણે '''karmarkar''' ફંકશન ઉપયોગ કરીને આપેલ ઉદાહરણોને હલ કરીશું: |
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| − | | | + | | આપેલ ઇક્વેશન ના માટે મીનીમાઇઝ '''minus three 'x' one minus 'x' two minus three 'x' three''' મીનીમાઇઝ કરો. |
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Revision as of 12:23, 14 March 2016
| Time | Narration |
| 00:01 | નમસ્તે મિત્રો, |
| 00:02 | Scilab ઉપયોગ કરીને Optimization of Linear Functions with Linear Constraints પરના સ્પોકન ટ્યુટોરીયલમાં તમારું સ્વાગત છે. |
| 00:10 | આ ટ્યુટોરીયલમ્ક આપને શીખ્યા : |
| 00:12 | Optimization નો અર્થ શું છે ? |
| 00:15 | અને ઓપ્ટિમાઇઝેશન માં Scilab function karmarkar ને કેવી રીતે ઉપયોગ કરે છે. |
| 00:20 | Optimization નો અર્થ |
| 00:22 | આપેલ objective function ને મીનીમાઇઝ અથવા મેક્સીમાઈઝ કરવાનું છે. |
| 00:26 | ડીસીજ્ન વેરીએબલને જુદું કરીને ક્યારે ક્યારે આને Cost function પણ કરે છે. |
| 00:33 | પૂર્વવ્યાખ્યાયિત constraints ના અનુસાર ડીસીજ્ન વેરીએબલને બદલવાય છે. |
| 00:38 | આ constraints વેરીએબલના અમુક ફંકશન ના ફોર્મમાં પણ હોય છે. |
| 00:44 | Optimization વ્યાપક રૂપથી આપેલ એન્જીનિયરિંગ અને નોન-એન્જીનિયરિંગ ક્ષેત્રો માં વધુ ઉપયોગ થાય છે. |
| 00:52 | ઇકોનોમિક્સ |
| 00:54 | કન્ટ્રોલ થીયરી અને |
| 00:56 | ઓપરેશન્સ અને રિસર્ચ. |
| 00:58 | Scilab function karmarkar આપેલમાં ઉપયોગ થાય છે: |
| 01:01 | લીન્યર ઓબ્જેક્ટીવ ફંકશન ને ઓપ્ટીમાઈઝ કરવા માટે, |
| 01:05 | લીનીયર constraints પર |
| 01:07 | ડીસીજ્ન વેરીએબલસ પર. |
| 01:10 | આપણે karmarkar ફંકશન ઉપયોગ કરીને આપેલ ઉદાહરણોને હલ કરીશું: |
| 01:14 | આપેલ ઇક્વેશન ના માટે મીનીમાઇઝ minus three 'x' one minus 'x' two minus three 'x' three મીનીમાઇઝ કરો. |
| 01:19 | for: two 'x' one plus 'x' two plus 'x' three less than or equal to two. |
| 01:26 | 'x' one plus two 'x' two plus three 'x' three less than or equal to five. |
| 01:32 | two 'x' one plus two 'x' two plus 'x' three less than or equal to six. |
| 01:36 | where 'x' one 'x' two 'x' three are all greater than or equal to zero |
| 01:42 | Note that all the functions, objective functions as well as constraints, are linear. |
| 01:49 | Before we solve the given problem, go to scilab console and type: |
| 01:54 | help karmarkar |
| 01:57 | and press Enter. |
| 01:59 | You can see the calling sequence of the argument. |
| 02:03 | The argument explanation, description and some examples in the Help Browser. |
| 02:12 | Close the Help Browser . |
| 02:14 | We will summarize the input and output arguments here. |
| 02:19 | Output arguments are 'x' opt, 'f' opt, exitflag, iter, 'y' opt . |
| 02:25 | 'x' opt: is the optimum solution . |
| 02:28 | 'f' opt: is the objective function value at optimum solution |
| 02:33 | 'exitflag' : is the status of execution, it helps in identifying if the algorithm is converging or not. |
| 02:41 | 'iter' : is the number of iterations required to reach 'x' opt. |
| 02:46 | 'y' opt : is the structure containing the dual solution. |
| 02:49 | This gives the Lagrange multipliers. |
| 02:53 | Input arguments are 'Aeq' 'beq' 'c' 'x zero' 'rtolf 'gam' 'maxiter' 'outfun' 'A' 'b' 'lb' and 'ub' |
| 03:09 | 'Aeq' : is the Matrix in the linear equality constraints. |
| 03:12 | 'beq' :is the right hand side of the linear equality constraint. |
| 03:17 | 'c' : is the Linear objective function coefficients of 'x'. |
| 03:21 | 'x' zero : is the Initial guess . |
| 03:25 | rtolf : is Relative tolerance on 'f' of 'x' is equals to 'c' transpose multiplied by 'x'. |
| 03:34 | 'gam' : is the Scaling factor. |
| 03:36 | 'maxiter' : is the maximum number of iterations after which the output is returned. |
| 03:43 | 'outfun' : is the additional user-defined output function. |
| 03:47 | 'A' : is the Matrix of linear inequality constraints |
| 03:51 | 'b' : is the right hand side of the linear inequality constraints. |
| 03:55 | 'lb' : is the lowerbound of 'x'. |
| 03:58 | 'ub' are the upper bound of 'x'. |
| 04:02 | Now, we can solve the given example in Scilab using karmarkar function. |
| 04:07 | Go to the scilab console and type: |
| 04:11 | 'A' is equals to open square bracket, two <space> one <space> one <semicolon> one <space> two <space> three <semicolon> two <space> two <space> one, close the square bracket |
| 04:26 | and press Enter. |
| 04:28 | similarly type: small 'b' equals to open square bracket, two <semicolon> five <semicolon> six, close the square bracket. |
| 04:38 | and press Enter. |
| 04:41 | Type: 'c' equals to open square bracket, minus three <semicolon> minus one <semicolon> minus three, close the square bracket. |
| 04:53 | and press Enter. |
| 04:55 | Type: 'lb' equals to open square bracket, zero <semicolon> zero <semicolon> zero, close the square bracket. |
| 05:05 | and press Enter. |
| 05:07 | Now clear the console using clc command. |
| 05:12 | Type: open square bracket, 'x' opt <comma> 'f' opt <comma> 'exitflag' <comma> iter, close the square bracket equals to karmarkar open parenthesis, open square bracket, close the square bracket <comma> open square bracket, close the square bracket <comma> 'c' <comma> open square bracket, close the square bracket <comma> open square bracket, close the square bracket <comma> open square bracket, close the square bracket <comma> open square bracket, close the square bracket <comma> open square bracket, close the square bracket <comma> capital 'A' <comma> 'small b' <comma> 'lb', close the round bracket. |
| 06:09 | and Press Enter. |
| 06:11 | Press Enter to continue the display. |
| 06:14 | This will give the output as shown on the screen |
| 06:18 | where xopt is the optimum solution to the problem, |
| 06:23 | fopt is the value of the objective function, calculated at optimum solution x is equal to xopt |
| 06:32 | and number of iteration required to reach the optimum solution xopt is 70. |
| 06:39 | Please note that: it is mandatory to specify the input arguments in the same order |
| 06:46 | in which they have been listed above, while calling the function. |
| 06:51 | In this tutorial, we learned: |
| 06:53 | What is optimization? |
| 06:55 | Use of Scilab function karmarkar in optimization to solve linear problems. |
| 07:01 | To contact the scilab team, please write to contact@scilab.in |
| 07:08 | Watch the video available at the following link. |
| 07:10 | It summarizes the Spoken Tutorial project. |
| 07:14 | If you do not have good bandwidth, you can download and watch it. |
| 07:18 | The spoken tutorial project Team: |
| 07:20 | Conducts workshops using spoken tutorials. |
| 07:23 | Gives certificates to those who pass an online test. |
| 07:27 | For more details, please write to contact@spoken-tutorial.org. |
| 07:34 | Spoken Tutorial Project is a part of the Talk to a Teacher project. |
| 07:37 | It is supported by the National Mission on Eduction through ICT, MHRD, Government of India. |
| 07:44 | More information on this mission is available at spoken-tutorial.org/NMEICT-Intro. |
| 07:53 | This is Anuradha Amrutkar from IIT Bombay, signing off. |
| 07:57 | Thank you for joining. Good Bye. |
