Main
~~# ~~{~~`g~~_i(x*)-b_~~i`~~} ~~(Feasible constraints)~~

#{~~`~~\grad f(x*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x*\right)=~~0`~~} ~~(No direction improves objective and is feasible~~)

~~# {`~~\~~lambda_i* \left( g_i(x*)-b_i \right) = 0`~~} ~~(Complementary slackness)~~

#{~~`~~\lambda_i* \ge ~~0`~~}~~ (Positive Lagrange multipliers)~~
~~The~~ four KKT conditions for optimality ~~are~~

#{~~`g_i(x)-b_i`~~} ~~is feasible~~

#{`\~~grad f~~(x~~^~~*)-~~\sum~~_~~{i=1~~}~~^m \lambda_i^* \grad g_i~~\~~left~~(x~~^~~*~~\right~~)=~~0`~~}

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<iframe width="560" height="315" src="https://www.youtube.com/embed/ws38Jon_-_E?rel=0" frameborder="0" allowfullscreen></iframe>(:htmlend:)

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Attach:group50.png This assignment can be completed in groups of two. Additional guidelines on individual, collaborative, and group assignments are provided under the [[Main/CourseStandards | Expectations link]].
~~-> Attach~~:~~kkt_contour~~.~~png~~

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/JTTiELgMyuM?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

----

!!!! Part 3: 5 Minute KKT Exercise with both Inequality and Equality Constraints

This 5 minute exercise is similar to the previous ones but solves a problem with both equality and inequality constraints.

Download the following worksheet on KKT conditions with inequality and equality constraints. The video below reviews the solution to this worksheet.

* [[Attach:kkt_example3.pdf|KKT Conditions Worksheet 3]]

* [[Attach:kkt_example3_solution.pdf|KKT Conditions Worksheet 3 Solution]]

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<iframe width="560" height="315" src="https://www.youtube.com/embed/JTTiELgMyuM?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

----

!!!! Part 4: 5 Minute Application Exercise for the Optimal Volume of a Tank

This 5 minute exercise covers an application to a tank volume optimization. In this case, we specify the final Lagrange multiplier of $8/ft'^3^'.

Download the following worksheet on this application of the KKT conditions. The video below reviews the solution to this worksheet.

* [[Attach:kkt_example4.pdf|KKT Conditions Worksheet 4]]

* [[Attach:kkt_example4_solution.pdf|KKT Conditions Worksheet 4 Solution]]
~~* 2 Linear equality constraints~~

----

!!!! Tutorial on the KKT Conditions

This 5 minute introductory video reviews the 4 KKT conditions and applies them to solve a simple quadratic programming (QP) problem with:

* 1 Quadratic objective function

* 3 Variables (x'_1_', x'_2_', x'_3_')

* 2 Linear equality constraints

Download the following worksheet on KKT conditions. The video below reviews the solution to this worksheet.

* [[Attach:kkt_example1.pdf|Worksheet 1 on the KKT Conditions]]

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/eaKPzb11qFw?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

## Karush-Kuhn-Tucker (KKT) Conditions

## Main.KuhnTucker History

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The feasibility condition (1) applies to both equality and inequality constraints and is simply a statement that the constraints must not be violated at optimal conditions. The gradient condition (2) ensures that there is no feasible direction that could potentially improve the objective function. The last two ~~condition~~ (3 and 4) are only required with inequality constraints and enforce a positive Lagrange multiplier when the constraint is active (=0) and a zero Lagrange multiplier when the constraint is inactive (>0).

to:

The feasibility condition (1) applies to both equality and inequality constraints and is simply a statement that the constraints must not be violated at optimal conditions. The gradient condition (2) ensures that there is no feasible direction that could potentially improve the objective function. The last two conditions (3 and 4) are only required with inequality constraints and enforce a positive Lagrange multiplier when the constraint is active (=0) and a zero Lagrange multiplier when the constraint is inactive (>0).

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[[https://apmonitor.com/online/view_pass.php?f=qp3.apm | -> Attach:kkt_contour.png]]

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[[https://apmonitor.com/online/view_pass.php?f=qp3.apm | -> Attach:kkt_contour.png]]

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<iframe width="560" height="315" src="https://www.youtube.com/embed/eaKPzb11qFw~~?rel=0~~" frameborder="0" allowfullscreen></iframe>

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<iframe width="560" height="315" src="https://www.youtube.com/embed/JTTiELgMyuM~~?rel=0~~" frameborder="0" allowfullscreen></iframe>

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There are four KKT conditions for ~~optimality with the primal ~~{~~`x*~~`} ~~variable ~~and dual {`\lambda~~*~~`} ~~variable optimal~~ values.

to:

There are four KKT conditions for optimal primal {`(x)`} and dual {`(\lambda)`} variables. The asterisk (*) denotes optimal values.

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{$\quad\quad\quad g_i(x)-b_i = 0 \quad i=k+1,\ldots,m$}

to:

{$\quad\quad\quad\quad\quad g_i(x)-b_i = 0 \quad i=k+1,\ldots,m$}

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{$g_i(x^*)-b_i$}

to:

{$g_i(x^*)-b_i \mathrm{\;is\;feasible}$}

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{$\mathrm{subject\;to}\~~;~~g_i(x)-b_i \ge 0 \quad i=1,\ldots,k$}

{$\quad\quad g_i(x)-b_i = 0 \quad i=k+1,\ldots,m$}

{$\quad\quad g_i(x)-b_i = 0 \quad i=k+1,\ldots,m$}

to:

{$\mathrm{subject\;to}\quad g_i(x)-b_i \ge 0 \quad i=1,\ldots,k$}

{$\quad\quad\quad g_i(x)-b_i = 0 \quad i=k+1,\ldots,m$}

{$\quad\quad\quad g_i(x)-b_i = 0 \quad i=k+1,\ldots,m$}

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{$~~`~~\lambda_i^* \left( g_i(x^*)-b_i \right) = 0$}

to:

{$\lambda_i^* \left( g_i(x^*)-b_i \right) = 0$}

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{$\~~gradient~~ f(x^*)-\sum_{i=1}^m \lambda_i^* \~~grad~~ g_i\left(x^*\right)=0$}

to:

{$\nabla f(x^*)-\sum_{i=1}^m \lambda_i^* \nabla g_i\left(x^*\right)=0$}

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{$g_i(x*)-b_i$}

to:

{$g_i(x^*)-b_i$}

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{$\~~grad~~ f(x*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x*\right)=0$}

to:

{$\gradient f(x^*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x^*\right)=0$}

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{$`\lambda_i* \left( g_i(x*)-b_i \right) = 0$}

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{$`\lambda_i^* \left( g_i(x^*)-b_i \right) = 0$}

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{$\lambda_i* \ge 0$}

to:

{$\lambda_i^* \ge 0$}

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#

#

to:

!!!!! 1. Feasible Constraints

{$g_i(x*)-b_i$}

!!!!! 2. No Feasible Descent

{$\grad f(x*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x*\right)=0$}

!!!!! 3. Complementary Slackness

{$`\lambda_i* \left( g_i(x*)-b_i \right) = 0$}

!!!!! 4. Positive Lagrange Multipliers

{$\lambda_i* \ge 0$}

{$g_i(x*)-b_i$}

!!!!! 2. No Feasible Descent

{$\grad f(x*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x*\right)=0$}

!!!!! 3. Complementary Slackness

{$`\lambda_i* \left( g_i(x*)-b_i \right) = 0$}

!!!!! 4. Positive Lagrange Multipliers

{$\lambda_i* \ge 0$}

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#

#

to:

There are four KKT conditions for optimality with the primal {`x*`} variable and dual {`\lambda*`} variable optimal values.

# {`g_i(x*)-b_i`} (Feasible constraints)

# {`\grad f(x*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x*\right)=0`} (No direction improves objective and is feasible)

# {`\lambda_i* \left( g_i(x*)-b_i \right) = 0`} (Complementary slackness)

# {`\lambda_i* \ge 0`} (Positive Lagrange multipliers)

The feasibility condition (1) applies to both equality and inequality constraints and is simply a statement that the constraints must not be violated at optimal conditions. The gradient condition (2) ensures that there is no feasible direction that could potentially improve the objective function. The last two condition (3 and 4) are only required with inequality constraints and enforce a positive Lagrange multiplier when the constraint is active (=0) and a zero Lagrange multiplier when the constraint is inactive (>0).

# {`g_i(x*)-b_i`} (Feasible constraints)

# {`\grad f(x*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x*\right)=0`} (No direction improves objective and is feasible)

# {`\lambda_i* \left( g_i(x*)-b_i \right) = 0`} (Complementary slackness)

# {`\lambda_i* \ge 0`} (Positive Lagrange multipliers)

The feasibility condition (1) applies to both equality and inequality constraints and is simply a statement that the constraints must not be violated at optimal conditions. The gradient condition (2) ensures that there is no feasible direction that could potentially improve the objective function. The last two condition (3 and 4) are only required with inequality constraints and enforce a positive Lagrange multiplier when the constraint is active (=0) and a zero Lagrange multiplier when the constraint is inactive (>0).

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The necessary conditions for a constrained local optimum are called the Kuhn~~-Tucker Conditions, and these conditions play a very important role in constrained optimization theory and algorithm~~ development.

to:

The necessary conditions for a constrained local optimum are called the Karush Kuhn Tucker (KKT) Conditions, and these conditions play a very important role in constrained optimization theory and algorithm development. For an optimization problem:

{$\min_x f(x)$}

{$\mathrm{subject\;to}\;g_i(x)-b_i \ge 0 \quad i=1,\ldots,k$}

{$\quad\quad g_i(x)-b_i = 0 \quad i=k+1,\ldots,m$}

The four KKT conditions for optimality are

# {`g_i(x)-b_i`} is feasible

# {`\grad f(x^*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x^*\right)=0`}

{$\min_x f(x)$}

{$\mathrm{subject\;to}\;g_i(x)-b_i \ge 0 \quad i=1,\ldots,k$}

{$\quad\quad g_i(x)-b_i = 0 \quad i=k+1,\ldots,m$}

The four KKT conditions for optimality are

# {`g_i(x)-b_i`} is feasible

# {`\grad f(x^*)-\sum_{i=1}^m \lambda_i^* \grad g_i\left(x^*\right)=0`}

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----

!!!! Part 5: KKT Conditions for Dynamic Optimization

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<iframe width="560" height="315" src="https://www.youtube.com/embed/n4OzENKziR4" frameborder="0" allowfullscreen></iframe>

----

!!!! Part 5: KKT Conditions for Dynamic Optimization

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----

Attach:group50.png This assignment can be completed in groups of two. Additional guidelines on individual, collaborative, and group assignments are provided under the [[Main/CourseStandards | Expectations link]].

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[[https://apmonitor.com/online/view_pass.php?f=qp3.apm | -> Attach:kkt_contour.png]]

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<iframe width="560" height="315" src="https://www.youtube.com/embed/AQWy73cHoIU?rel=0" frameborder="0" allowfullscreen></iframe>

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!!!! 5 Minute Tutorial on the KKT Conditions

to:

!!!! Part 1: 5 Minute Tutorial on the KKT Conditions

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!!!! 5 Minute Tutorial with KKT Conditions and Inequality Constraints

to:

!!!! Part 2: 5 Minute Tutorial with KKT Conditions and Inequality Constraints

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(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/JTTiELgMyuM?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

----

!!!! Part 3: 5 Minute KKT Exercise with both Inequality and Equality Constraints

This 5 minute exercise is similar to the previous ones but solves a problem with both equality and inequality constraints.

Download the following worksheet on KKT conditions with inequality and equality constraints. The video below reviews the solution to this worksheet.

* [[Attach:kkt_example3.pdf|KKT Conditions Worksheet 3]]

* [[Attach:kkt_example3_solution.pdf|KKT Conditions Worksheet 3 Solution]]

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/JTTiELgMyuM?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

----

!!!! Part 4: 5 Minute Application Exercise for the Optimal Volume of a Tank

This 5 minute exercise covers an application to a tank volume optimization. In this case, we specify the final Lagrange multiplier of $8/ft'^3^'.

Download the following worksheet on this application of the KKT conditions. The video below reviews the solution to this worksheet.

* [[Attach:kkt_example4.pdf|KKT Conditions Worksheet 4]]

* [[Attach:kkt_example4_solution.pdf|KKT Conditions Worksheet 4 Solution]]

Changed line 7 from:

* [[Attach:kuhn_tucker_hw.pdf|Kuhn-Tucker and Lagrange Multiplier Homework]]

to:

* [[Attach:kuhn_tucker_hw.pdf|Karush-Kuhn-Tucker and Lagrange Multiplier Homework]]

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!!!! Tutorial on the KKT Conditions

to:

!!!! 5 Minute Tutorial on the KKT Conditions

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* 2 Linear equality constraints

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to:

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* [[Attach:kkt_example1.pdf|~~Worksheet 1 on the~~ KKT Conditions]]

to:

* [[Attach:kkt_example1.pdf|KKT Conditions Worksheet 1]]

* [[Attach:kkt_example1_solution.pdf|KKT Conditions Worksheet 1 Solution]]

* [[Attach:kkt_example1_solution.pdf|KKT Conditions Worksheet 1 Solution]]

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----

!!!! 5 Minute Tutorial with KKT Conditions and Inequality Constraints

This next 5 minute introductory is similar to the previous one but solves a problem with inequality constraints instead of equality constraints. The problem is a simple quadratic programming (QP) problem with:

* 1 Quadratic objective function

* 2 Linear inequality constraints

* 3 Variables (x'_1_', x'_2_', x'_3_')

Download the following worksheet on KKT conditions with inequality constraints. The video below reviews the solution to this worksheet.

* [[Attach:kkt_example2.pdf|KKT Conditions Worksheet 2]]

* [[Attach:kkt_example2_solution.pdf|KKT Conditions Worksheet 2 Solution]]

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/JTTiELgMyuM?rel=0" frameborder="0" allowfullscreen></iframe>

----

!!!! 5 Minute Tutorial with KKT Conditions and Inequality Constraints

This next 5 minute introductory is similar to the previous one but solves a problem with inequality constraints instead of equality constraints. The problem is a simple quadratic programming (QP) problem with:

* 1 Quadratic objective function

* 2 Linear inequality constraints

* 3 Variables (x'_1_', x'_2_', x'_3_')

Download the following worksheet on KKT conditions with inequality constraints. The video below reviews the solution to this worksheet.

* [[Attach:kkt_example2.pdf|KKT Conditions Worksheet 2]]

* [[Attach:kkt_example2_solution.pdf|KKT Conditions Worksheet 2 Solution]]

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/JTTiELgMyuM?rel=0" frameborder="0" allowfullscreen></iframe>

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(:title Karush-Kuhn-Tucker (KKT) Conditions~~ and Lagrange Multipliers~~:)

to:

(:title Karush-Kuhn-Tucker (KKT) Conditions:)

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(:title Kuhn-Tucker Conditions and Lagrange Multipliers:)

(:keywords Kuhn-Tucker Conditions, Lagrange Multiplier, Optimization, Constrained:)

(:description Homework on Kuhn-Tucker conditions and Lagrange multipliers including a number of problems.:)

!!!! Kuhn-Tucker Conditions

(:keywords Kuhn-Tucker Conditions, Lagrange Multiplier, Optimization, Constrained:)

(:description Homework on Kuhn-Tucker conditions and Lagrange multipliers including a number of problems.:)

!!!! Kuhn-Tucker Conditions

to:

(:title Karush-Kuhn-Tucker (KKT) Conditions and Lagrange Multipliers:)

(:keywords Karush-Kuhn-Tucker Conditions, Lagrange Multiplier, Optimization, Constrained:)

(:description Homework on Karush-Kuhn-Tucker (KKT) conditions and Lagrange multipliers including a number of problems.:)

!!!! Karush-Kuhn-Tucker (KKT) Conditions

(:keywords Karush-Kuhn-Tucker Conditions, Lagrange Multiplier, Optimization, Constrained:)

(:description Homework on Karush-Kuhn-Tucker (KKT) conditions and Lagrange multipliers including a number of problems.:)

!!!! Karush-Kuhn-Tucker (KKT) Conditions

Added lines 10-27:

----

!!!! Tutorial on the KKT Conditions

This 5 minute introductory video reviews the 4 KKT conditions and applies them to solve a simple quadratic programming (QP) problem with:

* 1 Quadratic objective function

* 3 Variables (x'_1_', x'_2_', x'_3_')

* 2 Linear equality constraints

Download the following worksheet on KKT conditions. The video below reviews the solution to this worksheet.

* [[Attach:kkt_example1.pdf|Worksheet 1 on the KKT Conditions]]

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/eaKPzb11qFw?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

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(:title Kuhn-Tucker Conditions and Lagrange Multipliers:)

(:keywords Kuhn-Tucker Conditions, Lagrange Multiplier, Optimization, Constrained:)

(:description Homework on Kuhn-Tucker conditions and Lagrange multipliers including a number of problems.:)

!!!! Kuhn-Tucker Conditions

The necessary conditions for a constrained local optimum are called the Kuhn-Tucker Conditions, and these conditions play a very important role in constrained optimization theory and algorithm development.

* [[Attach:kuhn_tucker_hw.pdf|Kuhn-Tucker and Lagrange Multiplier Homework]]

----

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(:keywords Kuhn-Tucker Conditions, Lagrange Multiplier, Optimization, Constrained:)

(:description Homework on Kuhn-Tucker conditions and Lagrange multipliers including a number of problems.:)

!!!! Kuhn-Tucker Conditions

The necessary conditions for a constrained local optimum are called the Kuhn-Tucker Conditions, and these conditions play a very important role in constrained optimization theory and algorithm development.

* [[Attach:kuhn_tucker_hw.pdf|Kuhn-Tucker and Lagrange Multiplier Homework]]

----

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