Difference between revisions of "Scilab---FOSSEE-Optimisation-Toolbox/C2/Quadratic-Optimisation-using-FOT/English"
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<ul> | <ul> | ||
| − | <li><blockquote><p>Use '''fot underscore quadprog''' and '''fot underscore intquadprog''' | + | <li><blockquote><p>Use '''fot underscore quadprog''' and '''fot underscore intquadprog functions''' in '''Scilab'''</p></blockquote></li> |
| − | <li><blockquote><p>Solve quadratic '''optimization''' problems using, '''fot underscore quadprog''' and</p></blockquote></li></ul | + | <li><blockquote><p>Solve quadratic '''optimization''' problems using, '''fot underscore quadprog''' and '''fot underscore intquadprog functions'''</p></blockquote></li></ul> |
| − | + | ||
| − | + | ||
| − | + | ||
|- | |- | ||
| | | | ||
| Line 52: | Line 49: | ||
<ul> | <ul> | ||
<li><blockquote><p>Install '''FOSSEE Optimization Toolbox''' version '''0.4.1''' or above</p></blockquote></li> | <li><blockquote><p>Install '''FOSSEE Optimization Toolbox''' version '''0.4.1''' or above</p></blockquote></li> | ||
| − | <li><blockquote><p>Have basic understanding of Scilab and optimization theory</p></blockquote></li> | + | <li><blockquote><p>Have basic understanding of '''Scilab''' and '''optimization theory'''</p></blockquote></li> |
<li><blockquote><p>If not, for relevant tutorials please visit this site.</p></blockquote></li></ul> | <li><blockquote><p>If not, for relevant tutorials please visit this site.</p></blockquote></li></ul> | ||
|- | |- | ||
| Line 69: | Line 66: | ||
'''What is the Quadratic optimization problem?''' | '''What is the Quadratic optimization problem?''' | ||
| − | | | + | |What is the '''Quadratic optimization''' problem? |
| − | A function is quadratic if it has a degree of two. | + | A '''function''' is '''quadratic''' if it has a degree of two. |
| − | A '''quadratic optimization | + | A '''quadratic optimization''' problem is a mathematical '''optimization model'''. |
|- | |- | ||
| | | | ||
| Line 82: | Line 79: | ||
<ul> | <ul> | ||
| − | <li><blockquote><p>Quadratic objective function</p></blockquote></li> | + | <li><blockquote><p>'''Quadratic objective function'''</p></blockquote></li> |
| − | <li><blockquote><p>Bounds on the decision variables</p></blockquote></li> | + | <li><blockquote><p>'''Bounds''' on the '''decision variables'''</p></blockquote></li> |
| − | <li><blockquote><p>Linear constraints on decision variables</p></blockquote></li></ul> | + | <li><blockquote><p>Linear constraints on '''decision variables'''</p></blockquote></li></ul> |
|- | |- | ||
| | | | ||
| Line 90: | Line 87: | ||
'''Mathematical Formulation''' | '''Mathematical Formulation''' | ||
| − | | A | + | | A general form of the '''quadratic optimization''' problem is as shown. |
|- | |- | ||
| | | | ||
| Line 99: | Line 96: | ||
We will now solve this example to illustrate the use of '''fot underscore quadprog''' | We will now solve this example to illustrate the use of '''fot underscore quadprog''' | ||
| − | In this example, we will learn how to | + | In this example, we will learn how to minimize the given '''function'''. |
| − | + | ||
| − | + | ||
| − | + | ||
Note that the '''objective function''' is '''quadratic.''' | Note that the '''objective function''' is '''quadratic.''' | ||
| − | Note that there are bounds and linear constraints on the variables. | + | Note that there are '''bounds''' and linear constraints on the '''variables'''. |
|- | |- | ||
| | | | ||
| Line 113: | Line 107: | ||
'''Example''' | '''Example''' | ||
| | | | ||
| − | We will use the toolbox to solve this example. | + | We will use the '''toolbox''' to solve this example. |
I have downloaded the required files to my '''Downloads''' folder | I have downloaded the required files to my '''Downloads''' folder | ||
| Line 125: | Line 119: | ||
Point to the '''editor''' window. | Point to the '''editor''' window. | ||
| | | | ||
| − | In the '''Scilab console''' type '''editor''' and press '''Enter.''' | + | In the '''Scilab console,''' type '''editor''' and press '''Enter.''' |
'''Editor''' window opens. | '''Editor''' window opens. | ||
| Line 132: | Line 126: | ||
Click on Open button >> locate the file '''opt_quadprog .sce'''. | Click on Open button >> locate the file '''opt_quadprog .sce'''. | ||
| − | |||
| | | | ||
| − | Click on the '''Open''' button on the toolbar and locate the file '''opt_quadprog .sce'''. | + | Click on the '''Open''' button on the '''toolbar''' and locate the file '''opt_quadprog .sce'''. |
Then click the '''Ok''' button. | Then click the '''Ok''' button. | ||
| Line 141: | Line 134: | ||
|- | |- | ||
| Show '''opt_quadprog .sce''' in scilab editor. | | Show '''opt_quadprog .sce''' in scilab editor. | ||
| − | | Now we will see the input arguments for '''fot underscore quadprog.''' | + | | Now we will see the '''input arguments''' for '''fot underscore quadprog.''' |
|- | |- | ||
| Highlight '''‘nbVar’''' | | Highlight '''‘nbVar’''' | ||
| − | | '''nbVar''' is an integer denoting the number of '''decision variables'''. | + | | '''nbVar''' is an '''integer''' denoting the number of '''decision variables'''. |
|- | |- | ||
| Highlight '''‘nbCon’''' | | Highlight '''‘nbCon’''' | ||
| − | | ''' | + | | '''nbCon''' is an '''integer''' denoting the number of constraints. |
|- | |- | ||
| Highlight '''‘H’''' | | Highlight '''‘H’''' | ||
| Line 153: | Line 146: | ||
|- | |- | ||
| Highlight '''‘f’''' | | Highlight '''‘f’''' | ||
| − | | '''f''' is a '''vector,''' representing coefficients of the '''linear terms''' in the '''quadratic''' problem | + | | '''f''' is a '''vector,''' representing '''coefficients''' of the '''linear terms''' in the '''quadratic''' problem. |
|- | |- | ||
| Highlight '''‘x0’''' | | Highlight '''‘x0’''' | ||
| − | | '''x0''' is a vector containing the starting values of the decision variables. | + | | '''x0''' is a '''vector''' containing the starting values of the '''decision variables'''. |
|- | |- | ||
| Highlight the line with '''‘A’''' | | Highlight the line with '''‘A’''' | ||
| | | | ||
| − | '''''A''''' is a '''matrix''' of '''coefficients''' of | + | '''''A''''' is a '''matrix''' of '''coefficients''' of inequality constraints. |
| − | + | ||
| − | + | ||
|- | |- | ||
| Highlight the line with '''‘conUB’''' | | Highlight the line with '''‘conUB’''' | ||
| − | | '''conUB''' is the '''vector''' of the '''upper bounds''' of the | + | | '''conUB''' is the '''vector''' of the '''upper bounds''' of the constraints. |
|- | |- | ||
| Highlight the line with '''‘conLB’''' | | Highlight the line with '''‘conLB’''' | ||
| − | | '''conLB''' is the '''vector''' of the ''' | + | | '''conLB''' is the '''vector''' of the lower '''bounds''' of the constraints. |
|- | |- | ||
| Highlight '''lb''' and '''ub''' | | Highlight '''lb''' and '''ub''' | ||
| | | | ||
| − | '''lb''' and '''ub''' are row vectors. | + | '''lb''' and '''ub''' are row '''vectors'''. |
| − | They contain the lower and upper bounds of the decision variables respectively. | + | They contain the lower and upper '''bounds''' of the '''decision variables''' respectively. |
|- | |- | ||
| Point to the Output arguments. | | Point to the Output arguments. | ||
| | | | ||
| − | Now we will see the output arguments. | + | Now we will see the '''output arguments'''. |
| − | Output arguments are '''xopt,''' '''fopt''', '''exitflag, output and lambda.''' | + | '''Output arguments''' are '''xopt,''' '''fopt''', '''exitflag, output''' and '''lambda.''' |
|- | |- | ||
| Highlight '''‘xopt’''' | | Highlight '''‘xopt’''' | ||
| Line 186: | Line 177: | ||
|- | |- | ||
| Highlight '''‘fopt’''' | | Highlight '''‘fopt’''' | ||
| − | | '''fopt''' is the optimal | + | | '''fopt''' is the optimal '''objective function''' value. |
|- | |- | ||
| Highlight '''‘exitflag’''' | | Highlight '''‘exitflag’''' | ||
| − | | '''exitflag''' is the status of execution. | + | | '''exitflag''' is the status of '''execution'''. |
|- | |- | ||
| Highlight '''‘output’''' | | Highlight '''‘output’''' | ||
| − | | '''output''' is a '''structure''' containing detailed information about the optimization. | + | | '''output''' is a '''structure''' containing detailed information about the '''optimization'''. |
|- | |- | ||
| Highlight '''lambda''' | | Highlight '''lambda''' | ||
| Line 202: | Line 193: | ||
'''[xopt,fopt,exitflag,output,lambda]=fot_quadprog(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0)''' | '''[xopt,fopt,exitflag,output,lambda]=fot_quadprog(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0)''' | ||
| | | | ||
| − | Here we see the ''' | + | Here we see the '''Scilab''' code to define and solve the example. |
We call the '''fot underscore quadprog function''' to solve the given problem. | We call the '''fot underscore quadprog function''' to solve the given problem. | ||
| Line 209: | Line 200: | ||
Press '''CTRL + S''' | Press '''CTRL + S''' | ||
| − | |||
Click on the Execute button on scilab. | Click on the Execute button on scilab. | ||
| Line 219: | Line 209: | ||
To '''run''' the file, click on the '''Execute''' menu. | To '''run''' the file, click on the '''Execute''' menu. | ||
| − | Then click on the ''' | + | Then click on the '''File with echo''' from the drop down. |
|- | |- | ||
| | | | ||
| Line 232: | Line 222: | ||
| Change the window to Scilab console | | Change the window to Scilab console | ||
| | | | ||
| − | Switch to the | + | Switch to the '''Scilab console''' to see the '''output'''. |
The result displays “'''Optimal Solution Found.”''' | The result displays “'''Optimal Solution Found.”''' | ||
| − | We see that it prints the '''fopt''' value, '''xopt''' values, '''exitflag, output, and lambda''' in the '''Scilab console'''. | + | We see that it prints the '''fopt''' value, '''xopt''' values, '''exitflag, output,''' and '''lambda''' in the '''Scilab console'''. |
|- | |- | ||
| | | | ||
| Line 245: | Line 235: | ||
<ul> | <ul> | ||
<li><blockquote><p>We will now look at '''integer quadratic programming''' problems.</p></blockquote></li> | <li><blockquote><p>We will now look at '''integer quadratic programming''' problems.</p></blockquote></li> | ||
| − | <li><blockquote><p>These are problems where some decision variables are constrained to be integers.</p></blockquote></li></ul> | + | <li><blockquote><p>These are problems where some '''decision variables''' are constrained to be '''integers'''.</p></blockquote></li></ul> |
|- | |- | ||
| | | | ||
| Line 251: | Line 241: | ||
'''Mathematical Formulation''' | '''Mathematical Formulation''' | ||
| − | | A | + | | A general form of the '''integer''' constrained '''quadratic optimization''' problem is as shown. |
|- | |- | ||
| | | | ||
| Line 260: | Line 250: | ||
We will now solve this example to illustrate the use of '''fot underscore intquadprog'''. | We will now solve this example to illustrate the use of '''fot underscore intquadprog'''. | ||
| − | In this example, we will learn how to | + | In this example, we will learn how to minimize the given '''function'''. |
| − | + | ||
| − | + | ||
| − | + | ||
Note that the '''objective function''' is '''quadratic.''' | Note that the '''objective function''' is '''quadratic.''' | ||
| − | Note that some of the '''decision variables''' are | + | Note that some of the '''decision variables''' are constrained to be '''integers'''. |
|- | |- | ||
| Show '''opt_intquadprog.sce''' in scilab editor. | | Show '''opt_intquadprog.sce''' in scilab editor. | ||
| | | | ||
| − | We will use the toolbox to solve this example. | + | We will use the '''toolbox''' to solve this example. |
Open the '''Scilab console'''. | Open the '''Scilab console'''. | ||
| − | Type '''editor''' on the Scilab console and | + | Type '''editor''' on the '''Scilab console''' and press '''Enter'''. |
Open '''opt_intquadprog.sce''' in the '''Scilab editor.''' | Open '''opt_intquadprog.sce''' in the '''Scilab editor.''' | ||
|- | |- | ||
| Highlight '''‘H’''' | | Highlight '''‘H’''' | ||
| − | | '''H''' is a '''symmetric matrix,''' representing the '''Hessian''' of the '''quadratic''' problem | + | | '''H''' is a '''symmetric matrix,''' representing the '''Hessian''' of the '''quadratic''' problem. |
|- | |- | ||
| Highlight '''‘f’''' | | Highlight '''‘f’''' | ||
| − | | '''f''' is a '''vector,''' representing coefficients | + | | '''f''' is a '''vector,''' representing '''coefficients''' of the linear terms in the '''quadratic''' problem. |
|- | |- | ||
| Highlight the line with '''‘A’''' | | Highlight the line with '''‘A’''' | ||
| | | | ||
| − | '''''A''''' is a '''matrix''' of '''coefficients''' of | + | '''''A''''' is a '''matrix''' of '''coefficients''' of inequality constraints. |
| − | + | ||
| − | + | ||
|- | |- | ||
| Highlight the line with '''b''' | | Highlight the line with '''b''' | ||
| − | | '''b''' is the '''vector''' of the RHS of the | + | | '''b''' is the '''vector''' of the RHS of the inequality constraints. |
|- | |- | ||
| Highlight the line with '''Aeq''' | | Highlight the line with '''Aeq''' | ||
| − | | '''Aeq''' is the '''matrix''' of the LHS of the | + | | '''Aeq''' is the '''matrix''' of the LHS of the equality constraints. |
|- | |- | ||
| Highlight the line with '''beq''' | | Highlight the line with '''beq''' | ||
| − | | '''beq''' is the '''vector''' of the RHS of the | + | | '''beq''' is the '''vector''' of the RHS of the equality constraints. |
|- | |- | ||
| Highlight '''‘intcon’''' | | Highlight '''‘intcon’''' | ||
| − | | '''intcon''' is a '''vector''' of the '''indices''' of integer variables | + | | '''intcon''' is a '''vector''' of the '''indices''' of '''integer variables'''. |
|- | |- | ||
| | | | ||
| − | | | + | |Now we will see the '''output arguments'''. |
| − | Now we will see the output arguments. | + | |
| − | + | '''Output arguments''' are '''xopt, fopt, exitflag, output''' | |
|- | |- | ||
| Highlight '''‘xopt’''' | | Highlight '''‘xopt’''' | ||
| Line 314: | Line 298: | ||
|- | |- | ||
| Highlight '''‘fopt’''' | | Highlight '''‘fopt’''' | ||
| − | | '''fopt''' is the optimal | + | | '''fopt''' is the optimal '''objective function''' value. |
|- | |- | ||
| Highlight '''‘exitflag’''' | | Highlight '''‘exitflag’''' | ||
| − | | '''exitflag''' is the status of execution | + | | '''exitflag''' is the status of '''execution'''. |
|- | |- | ||
| Highlight '''output''' | | Highlight '''output''' | ||
| − | | '''Output''' is a '''structure''' containing detailed information about the optimization. | + | | '''Output''' is a '''structure''' containing detailed information about the '''optimization'''. |
|- | |- | ||
| | | | ||
| Line 326: | Line 310: | ||
'''[xopt,fopt,exitflag,output]=fot_intquadprog(H,f,intcon,A,b,Aeq,beq)''' | '''[xopt,fopt,exitflag,output]=fot_intquadprog(H,f,intcon,A,b,Aeq,beq)''' | ||
| − | | This is how we | + | | This is how we '''call fot underscore intquadprog.''' |
|- | |- | ||
| | | | ||
Press '''CTRL + S''' | Press '''CTRL + S''' | ||
| − | + | ||
Click on the Execute button on scilab. | Click on the Execute button on scilab. | ||
| Line 339: | Line 323: | ||
Save the file by pressing '''Control''' and '''‘S’''' keys simultaneously. | Save the file by pressing '''Control''' and '''‘S’''' keys simultaneously. | ||
| − | To '''run''' the file, click on the '''Execute''' | + | To '''run''' the file, click on the '''Execute menu'''. |
| − | Then click on the ''' | + | Then click on the '''File with echo''' from the drop down. |
|- | |- | ||
| | | | ||
| Line 351: | Line 335: | ||
| Change the window to Scilab console | | Change the window to Scilab console | ||
| | | | ||
| − | Switch to the | + | Switch to the '''Scilab console''' to see the '''output'''. |
The result displays “'''Optimal Solution Found.”''' | The result displays “'''Optimal Solution Found.”''' | ||
| Line 367: | Line 351: | ||
<ul> | <ul> | ||
| − | <li><blockquote><p>Use '''fot underscore quadprog''' and '''fot underscore intquadprog''' | + | <li><blockquote><p>Use '''fot underscore quadprog''' and '''fot underscore intquadprog functions''' of the '''FOSSEE Optimization Toolbox'''.</p></blockquote></li> |
| − | <li><blockquote><p>Solve | + | <li><blockquote><p>Solve '''quadratic programming''' problems in '''Scilab'''.</p></blockquote></li></ul> |
|- | |- | ||
| | | | ||
| Line 378: | Line 362: | ||
<ul> | <ul> | ||
| − | <li><blockquote><p>Minimise the following objective function.</p></blockquote></li> | + | <li><blockquote><p>Minimise the following '''objective function'''.</p></blockquote></li> |
| − | <li><blockquote><p>Use '''fot underscore quadprog''' | + | <li><blockquote><p>Use '''fot underscore quadprog function''' subjected to the given constraint</p></blockquote></li></ul> |
|- | |- | ||
| | | | ||
| Line 388: | Line 372: | ||
The optimum value will be -8.22. | The optimum value will be -8.22. | ||
| − | The optimum solution will be the vector [0.66 1.33]. | + | The optimum solution will be the '''vector''' [0.66 1.33]. |
|- | |- | ||
| | | | ||
| Line 398: | Line 382: | ||
<ul> | <ul> | ||
| − | <li><blockquote><p>Minimise the following objective function.</p></blockquote></li> | + | <li><blockquote><p>Minimise the following '''objective function'''.</p></blockquote></li> |
| − | <li><blockquote><p>Use '''fot underscore intquadprog''' | + | <li><blockquote><p>Use '''fot underscore intquadprog function''' subjected to the given constraints.</p></blockquote></li></ul> |
|- | |- | ||
| | | | ||
| Line 408: | Line 392: | ||
The optimum value will be -20. | The optimum value will be -20. | ||
| − | The optimum solution can be either vector [4 1 1 0 1] or [4 1 2 0 1]. | + | The optimum solution can be either '''vector''' [4 1 1 0 1] or [4 1 2 0 1]. |
|- | |- | ||
| | | | ||
Revision as of 12:38, 16 November 2021
Title of the script: Quadratic Optimization
Author: Siddharth Agarwal and Mankrit Singh
Keywords: FOSSEE Optimization Toolbox, Integer Quadratic optimization, Quadratic optimization, OR, quadprog, intquadprog .
| Visual Cue | Narration |
|---|---|
|
Show Slide Title Slide |
Hello and welcome to the Spoken tutorial on Quadratic optimization. |
|
Show Slide Learning Objectives |
In this tutorial, we will learn how to:
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Show Slide System Requirement |
To record this tutorial, I am using
|
|
Show Slide Pre-requisites |
To follow this tutorial, you should
|
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Show Slide Code Files |
|
|
Show Slide What is the Quadratic optimization problem? |
What is the Quadratic optimization problem?
A function is quadratic if it has a degree of two. A quadratic optimization problem is a mathematical optimization model. |
|
Show Slide What is the Quadratic optimization problem? |
It has:
|
|
Show Slide Mathematical Formulation |
A general form of the quadratic optimization problem is as shown. |
|
Show Slide Example |
We will now solve this example to illustrate the use of fot underscore quadprog In this example, we will learn how to minimize the given function. Note that the objective function is quadratic. Note that there are bounds and linear constraints on the variables. |
|
Show Slide Example |
We will use the toolbox to solve this example. I have downloaded the required files to my Downloads folder |
| Cursor on the Scilab console. | Now open the Scilab console. |
|
Type editor >> press Enter. Point to the editor window. |
In the Scilab console, type editor and press Enter. Editor window opens. |
|
Click on Open button >> locate the file opt_quadprog .sce. |
Click on the Open button on the toolbar and locate the file opt_quadprog .sce. Then click the Ok button. opt_quadprog.sce file opens in the editor |
| Show opt_quadprog .sce in scilab editor. | Now we will see the input arguments for fot underscore quadprog. |
| Highlight ‘nbVar’ | nbVar is an integer denoting the number of decision variables. |
| Highlight ‘nbCon’ | nbCon is an integer denoting the number of constraints. |
| Highlight ‘H’ | H is a symmetric matrix, representing the Hessian of the quadratic problem. |
| Highlight ‘f’ | f is a vector, representing coefficients of the linear terms in the quadratic problem. |
| Highlight ‘x0’ | x0 is a vector containing the starting values of the decision variables. |
| Highlight the line with ‘A’ |
A is a matrix of coefficients of inequality constraints. |
| Highlight the line with ‘conUB’ | conUB is the vector of the upper bounds of the constraints. |
| Highlight the line with ‘conLB’ | conLB is the vector of the lower bounds of the constraints. |
| Highlight lb and ub |
lb and ub are row vectors. They contain the lower and upper bounds of the decision variables respectively. |
| Point to the Output arguments. |
Now we will see the output arguments. Output arguments are xopt, fopt, exitflag, output and lambda. |
| Highlight ‘xopt’ | xopt is the optimal value of x. |
| Highlight ‘fopt’ | fopt is the optimal objective function value. |
| Highlight ‘exitflag’ | exitflag is the status of execution. |
| Highlight ‘output’ | output is a structure containing detailed information about the optimization. |
| Highlight lambda | lambda is a structure containing the Lagrange multipliers at the optimal solution. |
|
Highlight [xopt,fopt,exitflag,output,lambda]=fot_quadprog(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB,x0) |
Here we see the Scilab code to define and solve the example. We call the fot underscore quadprog function to solve the given problem. |
|
Press CTRL + S
Select file with echo from the drop-down. |
Save the file by pressing Control and ‘S’ keys simultaneously. To run the file, click on the Execute menu. Then click on the File with echo from the drop down. |
|
Point to the Confirmation box. Click Yes to confirm. |
A confirmation box to clear the Console appears. Click on the Yes button to confirm. |
| Change the window to Scilab console |
Switch to the Scilab console to see the output. The result displays “Optimal Solution Found.” We see that it prints the fopt value, xopt values, exitflag, output, and lambda in the Scilab console. |
|
Show Slide Integer Quadratic Programming |
|
|
Show Slide Mathematical Formulation |
A general form of the integer constrained quadratic optimization problem is as shown. |
|
Show Slide Example |
We will now solve this example to illustrate the use of fot underscore intquadprog. In this example, we will learn how to minimize the given function. Note that the objective function is quadratic. Note that some of the decision variables are constrained to be integers. |
| Show opt_intquadprog.sce in scilab editor. |
We will use the toolbox to solve this example. Open the Scilab console. Type editor on the Scilab console and press Enter. Open opt_intquadprog.sce in the Scilab editor. |
| Highlight ‘H’ | H is a symmetric matrix, representing the Hessian of the quadratic problem. |
| Highlight ‘f’ | f is a vector, representing coefficients of the linear terms in the quadratic problem. |
| Highlight the line with ‘A’ |
A is a matrix of coefficients of inequality constraints. |
| Highlight the line with b | b is the vector of the RHS of the inequality constraints. |
| Highlight the line with Aeq | Aeq is the matrix of the LHS of the equality constraints. |
| Highlight the line with beq | beq is the vector of the RHS of the equality constraints. |
| Highlight ‘intcon’ | intcon is a vector of the indices of integer variables. |
| Now we will see the output arguments.
Output arguments are xopt, fopt, exitflag, output | |
| Highlight ‘xopt’ | xopt is the optimal value of x. |
| Highlight ‘fopt’ | fopt is the optimal objective function value. |
| Highlight ‘exitflag’ | exitflag is the status of execution. |
| Highlight output | Output is a structure containing detailed information about the optimization. |
|
Highlight [xopt,fopt,exitflag,output]=fot_intquadprog(H,f,intcon,A,b,Aeq,beq) |
This is how we call fot underscore intquadprog. |
|
Press CTRL + S
Click on the Execute button on scilab. Select file with echo from the drop-down. |
Save the file by pressing Control and ‘S’ keys simultaneously. To run the file, click on the Execute menu. Then click on the File with echo from the drop down. |
|
Point to the Confirmation box. Click Yes to confirm. |
In the confirmation box, click the Yes button to clear the Console. |
| Change the window to Scilab console |
Switch to the Scilab console to see the output. The result displays “Optimal Solution Found.” We see that it prints the fopt value, xopt values, exitflag and output in the Scilab console. |
|
Show Slide Summary |
This brings us to the end of this tutorial. Let us summarise. In this tutorial, we have learnt how to:
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Show Slide Assignment I |
As an assignment:
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Show Slide Assignment I |
The optimum value will be -8.22. The optimum solution will be the vector [0.66 1.33]. |
|
Show Slide Assignment II |
Here is another assignment problem:
|
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Show Slide Assignment II |
The optimum value will be -20. The optimum solution can be either vector [4 1 1 0 1] or [4 1 2 0 1]. |
|
Show Slide About Spoken Tutorial Project |
The video at the following link summarises the Spoken Tutorial project. Please download and watch it. |
|
Show Slide Spoken Tutorial Workshops |
The Spoken Tutorial Project Team conducts workshops and gives certificates. For more details, please write to us |
|
Show Slide Answers for THIS Spoken Tutorial |
Please post your timed queries in this forum. |
|
Show Slide FOSSEE Forum |
Please post your general and technical queries on Scilab in this forum. |
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Show Slide Textbook Companion project |
The FOSSEE team coordinates the Textbook Companion project. We give Certificates and Honorarium to the contributors. For more details, please visit this site.. |
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Show Slide Lab Migration |
The FOSSEE team coordinates the Lab Migration project. For more details, please visit this site. |
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Show Slide Acknowledgement |
Spoken Tutorial and FOSSEE projects are funded by MoE, Government of India. |
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Show Slide Thank you |
This is Mankrit Singh, a FOSSEE intern 2021, IIT Bombay signing off Thanks for joining. |
