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\quad \dot{x}(t) & = &  p \cdot x(t),\\
\quad \dot{x}(t) & = &  p \cdot x(t),\\
\quad \dot{G}(t) & = & p \cdot G(t) + x(t), \\
\quad \dot{G}(t) & = & p \cdot G(t) + x(t), \\
\quad \dot{F_{11}}(t) & = & w(t) \cdot G(t)^2, \\
\quad \dot{F}(t) & = & w(t) \cdot G(t)^2, \\
\quad \dot{z}(t) & = & w(t), \\
\quad \dot{z}(t) & = & w(t), \\
\quad x(0) &=& x_0, \\
\quad x(0) &=& x_0, \\
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   \end{array}
   \end{array}
</math>
</math>
</p>
== Parameters ==
These fixed values are used within the model:
<p>
<math>
  x_0 = 1; \quad t_f = 1; \quad \mathcal{W} = [0,1]; \quad M = 0.2; \quad p \in \{-0.5, -2\}
</math>
</p>
</p>


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Here is one local solution to the above control problem.
Here is one local solution to the above control problem.


== Source Code ==
<gallery caption="Reference solution plots" widths="500px" heights="250px" perrow="1">
 
Image:Toy OED.png| States and measurement control for different choices of <math>p</math>.
Model descriptions are available in
</gallery>
 
* [[:Category:AMPL | AMPL]] at [[Lotka Experimental Design (AMPL)]]
* [[:Category: VPLAN | VPLAN code]] at [[Lotka Experimental Design (VPLAN)]]
 
== Variants ==
 
There are several alternative formulations and variants of the above problem, in particular
 
* a prescribed time grid for the control function <bib id="Sager2006" />, see also [[Lotka Experimental Design (AMPL)]],
* no fishing, i.e., <math>u \equiv 0</math>,
* different fishing control functions for the two species,
* different parameters and start values.


== Miscellaneous and Further Reading ==
== Miscellaneous and Further Reading ==
The Lotka Volterra fishing problem was introduced by Sebastian Sager in a proceedings paper <bib id="Sager2006" /> and revisited in his PhD thesis <bib id="Sager2005" />. These are also the references to look for more details. The experimental design problem has been described in the habilitation thesis of Sager, <bib id="Sager2011d" />.
The Toy OED problem was introduced by Sebastian Sager in <bib id="Sager2013" />, which contains further details.


== References ==
== References ==
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[[Category:ODE model]]
[[Category:ODE model]]
[[Category:Bang bang]]
[[Category:Bang bang]]
[[Category:Population dynamics]]

Latest revision as of 13:57, 29 January 2026

Toy OED
State dimension: 1
Differential states: 4
Discrete control functions: 1


The Toy OED problem looks for an optimal measurement strategy to determine a single parameter in a one-dimensional ODE model, where can directly measure the single state.

The optimal integer control functions shows bang bang behavior.

Mathematical formulation

For a single parameter p the original initial value problem is given by x˙(t)=px(t),t[0,tf],x(0)=x0.

We assume both x0 and tf to be fixed and are only interested in when to measure, with an upper bound M on the measuring time. We can measure the state directly, i.e. h(x(t))=x(t). Thus, the experimental design problem simplifies to:

minx,G,F,z,w1/F(tf)subject tox˙(t)=px(t),G˙(t)=pG(t)+x(t),F˙(t)=w(t)G(t)2,z˙(t)=w(t),x(0)=x0,G(0)=F(0)=z(0)=0,w(t)𝒲,0Mz(tf)

Parameters

These fixed values are used within the model:

x0=1;tf=1;𝒲=[0,1];M=0.2;p{0.5,2}

Reference Solutions

Here is one local solution to the above control problem.

Miscellaneous and Further Reading

The Toy OED problem was introduced by Sebastian Sager in [Sager2013]Author: Sager, S.
Journal: SIAM Journal on Control and Optimization
Number: 4
Pages: 3181--3207
Title: Sampling Decisions in Optimum Experimental Design in the Light of Pontryagin's Maximum Principle
Url: http://mathopt.de/PUBLICATIONS/Sager2013.pdf
Volume: 51
Year: 2013
Link to Google Scholar
, which contains further details.

References

[Sager2013]Sager, S. (2013): Sampling Decisions in Optimum Experimental Design in the Light of Pontryagin's Maximum Principle. SIAM Journal on Control and Optimization, 51, 3181--3207Link to Google Scholar