qp1.gms : Standard QP Model

Description

```The first in a series of variations on the standard
QP formulation. The subsequent models exploit data
and problem structures to arrive at formulations that
information can be found at:

```

Reference

• Kalvelagen, E, Model Building with GAMS. forthcoming

Small Model of Type : NLP

Category : GAMS Model library

Main file : qp1.gms   includes :  qpdata.inc

``````\$title Standard QP Model (QP1,SEQ=171)

\$onText
The first in a series of variations on the standard
QP formulation. The subsequent models exploit data
and problem structures to arrive at formulations that
information can be found at:

Kalvelagen, E, Model Building with GAMS. forthcoming

de Wetering, A V, private communication.

Keywords: nonlinear programming, quadratic programming, finance
\$offText

\$include qpdata.inc

Set
d(days)   'selected days'
s(stocks) 'selected stocks';

Alias (s,t);

* select subset of stocks and periods
d(days)   = ord(days) > 1 and ord(days) < 31;
s(stocks) = ord(stocks) < 51;

Parameter
mean(stocks)          'mean of daily return'
dev(stocks,days)      'deviations'
covar(stocks,sstocks) 'covariance matrix of returns (upper)'
totmean               'total mean return';

mean(s)  = sum(d, return(s,d))/card(d);
dev(s,d) = return(s,d) - mean(s);

* calculate covariance
* to save memory and time we only compute the uppertriangular
* part as the covariance matrix is symmetric
covar(upper(s,t)) = sum(d, dev(s,d)*dev(t,d))/(card(d) - 1);
totmean           = sum(s, mean(s))/(card(s));

Variable
z         'objective variable'
x(stocks) 'investments';

Positive Variable x;

Equation
obj    'objective'
budget
retcon 'return constraint';

obj..    z =e= sum(upper(s,t), x(s)*covar(s,t)*x(t))
+  sum(lower(s,t), x(s)*covar(t,s)*x(t));

budget.. sum(s, x(s)) =e= 1.0;

retcon.. sum(s, mean(s)*x(s)) =g= totmean*1.25;

Model qp1 / all /;

* Some solvers need more memory
qp1.workFactor = 10;

solve qp1 using nlp minimizing z;

display x.l;
``````