Difference between revisions of "Lotka Volterra fishing problem"

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== Source Code ==
 
== Source Code ==
  
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<code><pre>
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<source lang="cpp">
 
   double ref0 = 1, ref1 = 1;                /* steady state with u == 0 */
 
   double ref0 = 1, ref1 = 1;                /* steady state with u == 0 */
  
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   rhs[1] = - xd[1] + xd[0]*xd[1] - p[1]*u[0]*xd[1];
 
   rhs[1] = - xd[1] + xd[0]*xd[1] - p[1]*u[0]*xd[1];
 
   rhs[2] = (xd[0]-ref0)*(xd[0]-ref0) + (xd[1]-ref1)*(xd[1]-ref1);
 
   rhs[2] = (xd[0]-ref0)*(xd[0]-ref0) + (xd[1]-ref1)*(xd[1]-ref1);
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</pre></code>
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</source>
  
 
== Miscellaneous ==
 
== Miscellaneous ==
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</graphviz>
 
</graphviz>
  
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Testing Syntax Highlighting
 
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<source lang="csharp">
 
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// Hello World in Microsoft C# ("C-Sharp").
 
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using System;
 
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class HelloWorld
 
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{
 
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    public static int Main(String[] args)
 
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    {
 
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        Console.WriteLine("Hello, World!");
 
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        return 0;
 
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    }
 
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}
 
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</source>
 
 
== External references ==
 
== External references ==
  
 
[[Category:ODE Model]]
 
[[Category:ODE Model]]

Revision as of 10:17, 4 July 2008

This problem was set up as a simple benchmark problem. Despite of its simple structure, the optimal solution contains a singular arcs, making the Lotka Volterra fishing problem an ideal candidate for benchmarking of algorithms.

In this problem the Lotka Volterra equations for a predator-prey system have been augmented by an additional linear term, relating to fishing by man.

Model dimensions and properties

The model has the following dimensions:

\begin{array}{rcl} n_x &=& 3\\ n_z &=& 0\\ n_u &=& 0\\ n_w &=& 1\\ n_p &=& 0\\ n_{\rho} &=& 0\\ n_c &=& 0\\ n_{r^\mathrm{i}} &=& 0\\ n_{r^\mathrm{e}} &=& 3 \end{array}

It is thus an ODE model with a single integer control function. The interior point equality conditions fix the initial values of the differential states.

Mathematical formulation

For t \in [t_0, t_f] the mixed-integer optimal control problem is given by

\begin{array}{llcl} \displaystyle \min_{x, w} & x_2(t_f) \\[1.5ex] \mbox{s.t.} & \dot{x}_0(t) & = & x_0(t) - x_0(t) x_1(t) - \; c_0 x_0(t) \; w(t), \\ & \dot{x}_1(t) & = & - x_1(t) + x_0(t) x_1(t) - \; c_1 x_1(t) \; w(t), \\ & \dot{x}_2(t) & = & (x_0(t) - 1)^2 + (x_1(t) - 1)^2, \\[1.5ex] & x(0) &=& x_0, \\ & w(t) &\in& \{0, 1\}. \end{array}

Initial values and parameters

\begin{array}{rcl} t_0 &=& 0\\ t_f &=& 12\\ c_0 &=& 0.4\\ c_1 &=& 0.2\\ x_0 &=& (0.5, 0.7, 0)^T \end{array}

Reference Solutions

States


The two differential states and corresponding adjoint variables in the indirect approach

Source Code

  double ref0 = 1, ref1 = 1;                 /* steady state with u == 0 */
 
  rhs[0] =   xd[0] - xd[0]*xd[1] - p[0]*u[0]*xd[0];
  rhs[1] = - xd[1] + xd[0]*xd[1] - p[1]*u[0]*xd[1];
  rhs[2] = (xd[0]-ref0)*(xd[0]-ref0) + (xd[1]-ref1)*(xd[1]-ref1);

Miscellaneous

Testing Graphviz

<graphviz border='frame' format='svg'> digraph G {Hello->World!} </graphviz>

External references

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