cclinpts.gms : Finding Optimal Breakpoints when linearizing a power utility function

**Description**

A power utility function with constant relative risk aversion is often used in economic and financial modelling. It may be desired, for computational reasons, to linearize this function when it is used together with constraints requiring integer variables. This model finds the optimal breakpoints for such a linearization for a given interval.

**Reference**

- Rasmussen K.M., Finding Optimal Breakpoints when linearizing a power utility function.

**Small Model of Type :** NLP

**Category :** GAMS Model library

**Main file :** cclinpts.gms

```
$title Finding Optimal Breakpoints when linearizing a power utility function (SEQ=373)
$onText
A power utility function with constant relative risk aversion is often used
in economic and financial modelling. It may be desired, for computational
reasons, to linearize this function when it is used together with constraints
requiring integer variables. This model finds the optimal breakpoints for
such a linearization for a given interval.
Author:
Kourosh Marjani Rasmussen, July 2011, Technical University of Denmark, Department
of Management Engineering, Section for Operations Research, Financial Engineering Group
Keywords: nonlinear programming, finance, linearization, optimal breakpoints,
power utility function
$offText
$set first s1
$set last s30
Scalar gamma 'relative risk aversion' / 2 /;
Set
j 'number of break points' / %first%*%last% /
first(j)
last(j);
Alias (j,jj);
first('%first%') = yes;
last('%last%') = yes;
* Start and end x-axis values for the linearization interval
Scalar
b0 'start point (positive)' / 5 /
bm 'end point' / 100 /;
abort$(b0 < 0) 'b0 should be a positive number', b0;
abort$(bm < b0) 'end point needs to be greater than start point', b0, bm;
Variable
ObjV 'the value of the objective function'
b(j) 'breakpoints'
fb(j) 'function values for the break points';
b.lo(j) = b0;
b.up(j) = bm;
Equation
object 'the area under the curve'
FBCalc(j) 'calculating the power function values';
object..
ObjV =e= sum(j$(not last(j)), [b('%last%') - b(j)]*[fb(j) - fb(j-1)])
+ 0.5*sum(j$(not first(j)), [b(j) - b(j-1)]*[fb(j) - fb(j-1)]);
FBCalc(j).. fb(j) =e= power(b(j),(1 - gamma))/(1 - gamma);
b.fx('%first%') = b0;
b.fx('%last%') = bm;
Model OptimalLinearization / all /;
solve OptimalLinearization using nlp maximizing ObjV;
```