Exponential transformation: Difference between revisions

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Author: Daphne Duvivier (dld237), Daniela Gil (dsg254), Jacqueline Jackson (jkj49, Sinclaire Mills (sm2795), Vanessa Nobre (vmn28) Fall 2021
Author: Daphne Duvivier (dld237), Daniela Gil (dsg254), Jacqueline Jackson (jkj49), Sinclaire Mills (sm2795), Vanessa Nobre (vmn28) Fall 2021




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where <math> c_k \geq 0 </math> and <math> x_n \geq0 </math>
where <math> c_k \geq 0 </math> and <math> x_n \geq0 </math>


A transformation of <math> x_n = e^u_i </math> is applied  
A transformation of <math> x_n = e^u_i </math> is applied reference https://link.springer.com/content/pdf/10.1023/A:1021708412776.pdf


The transformed function is presented as:


Transformed into convex MINLP
<math> f(u) = \sum_{k=1}^N c_k{{e_1}^{u}{a_1k}}{e_2}^{a_2k}....{e_n}^{a_nk} </math>




<math>
\ln c
</math>


== Proof ==  
== Proof ==  
Line 32: Line 30:


== Numerical Example ==
== Numerical Example ==
<math> {\frac{x_1^3}{x_2^4}} + {x_1^2} + {\sqrt[3]{x_2^2}} \leq 4</math>
Reformulating to exponents:
<math> {x_1^3}*{x_2^-4} + {x_1^2} + {x_2^(\fract{2}{3}}}</math>
Substituting <math> x_1 = e^{u_1}, x_2 = e^{u_2} </math>
<math> {{e^u_1}}^3 </math>
<math> </math>
<math> </math>
<math> </math>


== Applications ==
== Applications ==
Exponential transformation can be used for convexification of any Geometric NLP or MINLP that meet the criteria of equation (1). This is done by turning the problem into a nonlinear convex optimization problem through exponential transformation.
== Example of Convexification application ==
Proof of convexity with positive definite test of Hessian
https://www.princeton.edu/~chiangm/gp.pdf
pROOF THAT CHANGING IT DOESNT CHANGE THE BOUNDS OF THE PROBLEM https://link.springer.com/content/pdf/10.1023/A:1021708412776.pdf
Example:
Electrical Engineering Application: http://home.eng.iastate.edu/~cnchu/pubs/j08.pdf




Quadratic Geometric Programming
Ecconomics: https://link.springer.com/content/pdf/10.1007/BF02591746.pdf


== Conclusion ==
== Conclusion ==
Exponential transformation is a powerful method to linearize
Exponential transformation is a powerful method to convexify Geometric NLP/MINLP to simplify the solution approach.
 


== References ==
== References ==
<references />
<references />

Revision as of 14:57, 27 November 2021

Author: Daphne Duvivier (dld237), Daniela Gil (dsg254), Jacqueline Jackson (jkj49), Sinclaire Mills (sm2795), Vanessa Nobre (vmn28) Fall 2021


Introduction

Exponential transformations are used for convexification of geometric programming constraints (posynominal) nonconvex optimization problems.


Geometric Programming


Theory & Methodology

Exponential transformation begins with a posynominal noncovex function of the form [1] :

where and

A transformation of is applied reference https://link.springer.com/content/pdf/10.1023/A:1021708412776.pdf

The transformed function is presented as:


Proof

Numerical Example

Reformulating to exponents: Failed to parse (unknown function "\fract"): {\displaystyle {x_1^3}*{x_2^-4} + {x_1^2} + {x_2^(\fract{2}{3}}}}

Substituting


Applications

Exponential transformation can be used for convexification of any Geometric NLP or MINLP that meet the criteria of equation (1). This is done by turning the problem into a nonlinear convex optimization problem through exponential transformation.


Example of Convexification application

Proof of convexity with positive definite test of Hessian


https://www.princeton.edu/~chiangm/gp.pdf

pROOF THAT CHANGING IT DOESNT CHANGE THE BOUNDS OF THE PROBLEM https://link.springer.com/content/pdf/10.1023/A:1021708412776.pdf

Example:


Electrical Engineering Application: http://home.eng.iastate.edu/~cnchu/pubs/j08.pdf


Quadratic Geometric Programming Ecconomics: https://link.springer.com/content/pdf/10.1007/BF02591746.pdf

Conclusion

Exponential transformation is a powerful method to convexify Geometric NLP/MINLP to simplify the solution approach.

References