Difference between revisions of "Scilab/C4/Optimization-Using-Karmarkar-Functions/English-timed"

Latest revision as of 11:07, 10 March 2017

Time	Narration
00:01	Dear Friends, 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 predefined 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 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.

Contributors and Content Editors

Gaurav, PoojaMoolya, Sandhya.np14

Difference between revisions of "Scilab/C4/Optimization-Using-Karmarkar-Functions/English-timed"

Latest revision as of 11:07, 10 March 2017

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@@ Line 1: / Line 1: @@
 {| Border=1
-|| Time
+|'''Time'''
-|| Narration
+|'''Narration'''
 |-
-| 00.01
+| 00:01
-|Dear Friends,
+|Dear Friends,  Welcome to the spoken tutorial on '''Optimization of Linear Functions with Linear Constraints Using Scilab'''.
 |-
-| 00.02
+| 00:10
-| Welcome to the spoken tutorial on '''Optimization of Linear Functions with Linear Constraints Using Scilab'''
+| In this tutorial, We will learn:
 |-
-| 00.10
+|00:12
-| In this tutorial, We will learn
+|what is meant by '''Optimization?'''
 |-
-|00.12
+|00:15
-|What is meant by '''Optimization?'''
+|and how to use '''Scilab function karmarkar''', for optimization.
 |-
-|00.15
+| 00:20
-|And How to use Scilab function karmarkar for optimization.
-|-
-| 00.20
 |'''Optimization''' means
 |-
-|00.22
+|00:22
-|Minimize or maximize a given '''objective function.'''
+|minimize or maximize a given '''objective function'''
 |-
-| 00.26
+| 00:26
-|Which is also called as '''Cost function''' sometimes.
+|which is also called as '''Cost function''' sometimes,
 |-
-| 00.30
+| 00:30
-| By varying the decision variables
+| by varying the decision variables.
 |-
-|00.33
+|00:33
-|The decision variables are varied subject to the pre-defined constraints.
+|The decision variables are varied subject to the predefined constraints.
 |-
-|00.38
+|00:38
 |These constraints are also in the form of some functions of the variables.
 |-
-| 00.44
+| 00:44
-| '''Optimization''' is extensively used in majority of the engineering as well as non-engineering fields like
+| '''Optimization''' is extensively used in majority of the engineering as well as non-engineering fields like:
 |-
-| 00.52
+| 00:52
 | Economics
 |-
-|00.54
+|00:54
 |Control Theory and
 |-
-|00.56
+|00:56
 | Operations & Research.
 |-
+|00:58
-|00.58
+|The '''Scilab function karmarkar''' is used for
-|The Scilab function Karmarkar is used for
 |-
+|01:01
-|01.01
+|optimizing the linear objective function,
-|Optimizing the linear objective function
 |-
+| 01:05
-| 01.05
 |subject to linear constraints
 |-
+| 01:07
-| 01.07
+||on the decision variables.
-||on the decision variables
 |-
+|01:10
-|01.10
+|| We will solve the following example using '''karmarkar''' function:
-|| We will solve the following example using ''' karmarkar'''  function:
 |-
+|01:14
-|01.14
 | Minimize '''minus three 'x' one minus 'x' two minus three 'x' three'''
 |-
-|01.19
+|01:19
-|for  '''two 'x' one plus 'x' two plus 'x' three  less than or equal to two.'''
+|for:  '''two 'x' one plus 'x' two plus 'x' three  less than or equal to two.'''
 |-
+|01:26
-|01.26
 |''' 'x' one plus two 'x' two plus three 'x' three  less than or equal to five.'''
 |-
+|01:32
-|01.32
 ||'''two 'x' one plus two 'x' two plus 'x' three  less than or equal to six.'''
 |-
-|01.36
+|01:36
 |where ''' 'x' one 'x' two 'x' three''' are all '''greater than''' or '''equal to zero'''
 |-
+| 01:42
-| 01.42
+|Note that all the functions, objective functions as well as constraints, are linear.
-|Note that all the functions objective functions as well as constraints are linear
 |-
+|01:49
-|01.49
+||Before we solve the given problem, go to '''scilab console''' and type:
-||Before we solve the given problem go to '''scilab console''' and type
 |-
+|01:54
-|01.54
 | '''help karmarkar'''
 |-
+|01:57
-|01.57
+| and press '''Enter.'''
-| and '''press Enter.'''
 |-
+| 01:59
-| 01.59
 ||You can see the calling sequence of the argument.
 |-
-| 02.03
+| 02:03
-|The argument explaination, description and some examples in the '''help browser.'''
+|The argument explanation, description and some examples in the '''Help Browser.'''
 |-
-|02.12
+|02:12
-| Close the '''help browser '''
+| Close the '''Help Browser '''.
 |-
-|02.14
+|02:14
-| We will summarize the input and output arguments here
+| We will summarize the input and output arguments here.
 |-
-| 02.19
+| 02:19
-|Out put arguments are ''' 'x' opt, 'f' opt, exitflag, iter, 'y' opt '''
+|Output arguments are ''' 'x' opt, 'f' opt, exitflag, iter, 'y' opt '''.
 |-
-| 02.25
+| 02:25
 |''' 'x' opt:''' is the optimum solution .
 |-
-| 02.28
+| 02:28
-|''''f' opt:'''  is the objective function value at '''optimum solution'''
+|''' 'f' opt:'''  is the objective function value at '''optimum solution'''
 |-
-| 02.33
+| 02:33
 |''' 'exitflag' ''': is the status of execution, it helps in identifying if the algorithm is converging or not.
 |-
-|02.41
+|02:41
-|''' 'iter' ''': Is the number of iterations required to reach ''' 'x' opt.'''
+|''' 'iter' ''': is the number of iterations required to reach ''' 'x' opt.'''
 |-
-|02.46
+|02:46
-|''' 'y' opt''' : is the structure containing the dual solution
+|''' 'y' opt''' : is the structure containing the dual solution.
 |-
+| 02:49
-| 02.49
 |This gives the Lagrange multipliers.
 |-
+| 02:53
-| 02.53
+||Input arguments are ''' 'Aeq' 'beq' 'c' 'x zero' 'rtolf 'gam' 'maxiter' 'outfun' 'A' 'b' 'lb' and 'ub' '''
-||Input arguments are ''' 'Aeq' 'beq '    'c' 'x' zero 'rtolf 'gam' 'maxiter' 'outfun' 'A' 'b' 'lb' and 'ub' '''
-'
 |-
+|03:09
-|03.09
 || ''' 'Aeq' ''' : is the Matrix in the linear equality constraints.
 |-
+| 03:12
-| 03.12
+| ''' 'beq' '''  :is the right hand side of the linear '''equality''' constraint.
-| ''' 'beq' '''  :is the right hand side of the linear '''equality''' constraints.
 |-
+| 03:17
-| 03.17
+|''' 'c' ''' : is the '''Linear objective function''' coefficients of ''' 'x'. '''
-|''' 'c' ''' : is the '''Linear objective function''' co-efficients of  ''' 'x'. '''
 |-
+| 03:21
-| 03.21
 | ''' 'x' zero''' : is the '''Initial guess .'''
 |-
+|03:25
-|03.25
 ||''' rtolf ''': is Relative tolerance on ''' 'f' of 'x' is  equals to 'c' transpose multiplied by 'x'. '''
 |-
+|03:34
-|03.34
 |''' 'gam' ''' : is the Scaling factor.
 |-
+| 03:36
-| 03.36
+|''' 'maxiter' ''' : is the ''' maximum''' number of iterations after which the output is returned.
-|''' 'maxiter' ''' : is the ''' Maximum''' number of iterations after which the output is returned.
 |-
+| 03:43
-| 03.43
+|''' 'outfun' ''' : is the additional user-defined output function.
-|''' 'outfun' ''' : is the additional user-defined output functions .
 |-
+| 03:47
-| 03.47
 | ''' 'A' ''': is the Matrix of linear inequality constraints
 |-
+| 03:51
-| 03.51
 | ''' 'b' ''': is the right hand side of the linear ''' inequality''' constraints.
 |-
+| 03:55
-| 03.55
 ||''' 'lb' ''': is the ''' lowerbound''' of ''' 'x'.'''
 |-
+| 03:58
-| 03.58
+||''' 'ub'''' are the '''upper bound'''  of ''' 'x'. '''
-||''' 'ub'''' are the '''Upper bounds'''  of ''' 'x'. '''
 |-
+| 04:02
-| 04.02
+||Now, we can solve the given example in Scilab using '''karmarkar''' function.
-||Now, we can now solve the given example in Scilab using ''' karmarkar''' function.
 |-
+| 04:07
-| 04.07
+|Go to the ''' scilab console''' and type:
-|Go to the ''' scilab console and type'''
 |-
+| 04:11
-| 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
-|'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
-|04.26
+|and press Enter.
-|And press Enter
 |-
+| 04:28
-| 04.28
+|similarly type: small 'b' equals to open square bracket, two <semicolon> five <semicolon> six, close the square bracket.
-|similarly type, small 'b' equals to open square bracket, two <semicolon>five <semicolon> six, close the square bracket.
 |-
+| 04:38
-| 04.38
+| and press '''Enter'''.
-| And press '''Enter'''
 |-
+| 04:41
-| 04.41
+| Type: 'c' equals to open square bracket, minus three <semicolon> minus one <semicolon> minus three, close the square bracket.
-| Type 'c' equals to open square bracket, minus three <semicolon> minus one <semicolon> minus three, close the square bracket.
 |-
+| 04:53
-| 04.53
+|and press ''' Enter'''.
-|And press ''' Enter'''
 |-
-| 04.55
+| 04:55
-| Type 'lb' equals to open square bracket, zero <semicolon> zero <semicolon> zero, close the square bracket.
+| Type: 'lb' equals to open square bracket, zero <semicolon> zero <semicolon> zero, close the square bracket.
 |-
-| 05.05
+| 05:05
-|And press '''Enter'''
+|and press '''Enter'''.
 |-
+|05:07
-| 05.07
+|Now clear the console using '''clc''' command.
-|Now clear the '''Scilab console using  clc command.'''
 |-
+| 05:12
-| 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.'''
-| 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
-| 06.09
+| and Press '''Enter'''.
-| And Press '''enter'''
 |-
+| 06:11
-| 06.11
+| Press Enter to continue the display.
-| Press Enter to continue the Display
 |-
+| 06:14
-| 06.14
+| This will give the output as shown on the screen
-| This will give the output as shown on the screen.
 |-
+| 06:18
-| 06.18
+| where '''xopt''' is the ''' optimum solution''' to the problem,
-| Where '''xopt'''  is the ''' optimal solution''' to the problem
 |-
+| 06:23
-| 06.23
+|'''fopt'''  is the value of the objective function, calculated at optimum solution x is equal to '''xopt'''
-|'''fopt'''  is the value of the objective function calculated at optimum solution x is equal to '''xopt'''
 |-
+| 06:32
-| 06.32
+|and number of iteration required to reach the optimum solution '''xopt''' is '''70'''.
-|and number of iteration required to reach the optimum solution ''' xopt is 70'''
 |-
+| 06:39
-| 06.39
+|Please note that: it is mandatory to specify the input arguments in the same order
-|Please note that: it is mandatory to specify the input arguments in the same order.
 |-
+| 06:46
-| 06.46
+|in which they have been listed above, while calling the function.
-|In which they have been listed above, while calling the function
 |-
+| 06:51
-| 06.51
+|In this tutorial, we learned:
-|In this tutorial, we learned
 |-
+| 06:53
-| 06.53
+|What is ''' optimization?'''
-|What is ''' Optimization?'''
 |-
+| 06:55
-| 06.55
+|Use of '''Scilab function karmarkar''' in optimization to solve linear problems.
-|Use of Scilab function karmarkar in optimization to solve linear problems.
 |-
+| 07:01
-| 07.01
+|To contact the scilab team, please write to '''contact@scilab.in'''
-|To contact the scilab team, please write to ''' contact@scilab.in '''
 |-
-|07.08
+|07:08
-| Watch the video available at the following link
+| Watch the video available at the following link.
 |-
+| 07:10
-| 07.10
+| It summarizes the Spoken Tutorial project.
-| It summarises the Spoken Tutorial project
 |-
+|07:14
-|07.14
+||If you do not have good bandwidth, you can download and watch it.
-||If you do not have good bandwidth, you can download and watch it
 |-
+|07:18
-|07.18
+||The spoken tutorial project Team:
-||The spoken tutorial project Team
 |-
+|07:20
-|07.20
+||Conducts workshops using spoken tutorials.
-||Conducts workshops using spoken tutorials
 |-
+|07:23
-|07.23
+||Gives certificates to those who pass an online test.
-||Gives certificates to those who pass an online test
 |-
+|07:27
-|07.27
+||For more details, please write to contact@spoken-tutorial.org.
-||For more details, please write to contact@spoken-tutorial.org
 |-
+|07:34
-|07.34
+|Spoken Tutorial Project is a part of the Talk to a Teacher project.
-|Spoken Tutorial Project is a part of the Talk to a Teacher project
 |-
+| 07:37
-| 07.37
 | It is supported by the National Mission on Eduction through ICT, MHRD, Government of India.
 |-
+| 07:44
-| 07.44
+|More information on this mission is available at spoken-tutorial.org/NMEICT-Intro.
-|More information on this mission is available at spoken-tutorial.org/NMEICT-Intro
 |-
+| 07:53
-| 07.53
+|This is Anuradha Amrutkar from IIT Bombay, signing off.
-|This is Anuradha Amrutkar from IIT Bombay signing off.
 |-
+|07:57
-|07.57
+| Thank you for joining. Good Bye.
-| Thank you for joining Good Bye.