Scilab/C4/Optimization-Using-Karmarkar-Functions/English-timed

From Script | Spoken-Tutorial
Revision as of 10:46, 11 July 2014 by Gaurav (Talk | contribs)

Jump to: navigation, search
Time Narration
00:01 Dear Friends,
00:02 Welcome to the spoken tutorial on Optimization of Linear Functions with Linear Constraints Using Scilab


00:10 In this tutorial, We will learn
00:12 What is meant by Optimization?


00:15 And How to use Scilab function karmarkar for optimization.


00:20 Optimization means
00:22 Minimize or maximize a given objective function.


00:26 Which is also called as Cost function sometimes.
00:30 By varying the decision variables
00:33 The decision variables are varied subject to the pre-defined constraints.
00:38 These constraints are also in the form of some functions of the variables.
00:44 Optimization is extensively used in majority of the engineering as well as non-engineering fields like
00:52 Economics
00:54 Control Theory and
00:56 Operations & Research.
00:58 The Scilab function Karmarkar is used for
01:01 Optimizing the linear objective function


01:05 subject to linear constraints


01:07 on the decision variables
01:10 We will solve the following example using karmarkar function:


01:14 Minimize minus three 'x' one minus 'x' two minus three 'x' three


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 explaination, 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 Out put 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 constraints.


03:17 'c'  : is the Linear objective function co-efficients 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 functions .


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 bounds of 'x'.
04:02 Now, we can now 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 Scilab 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 optimal 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 summarises 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.

Contributors and Content Editors

Gaurav, PoojaMoolya, Sandhya.np14