Functions
	pindyck.PDModelData.buildModel (self)
	adding the variables and constraints to the model

	pindyck.PDModelData.evaluateNonlinearTerm (self, x, rowno, ignerr, thread)
	callback method for evaluating the nonlinear terms in a given row

	pindyck.PDModelData.evaluateNonlinearJacobian (self, x, rowno, jacnum, ignerr, thread)
	callback method for evaluating the jacobian for the nonlinear terms in a given row

Detailed Description

This is a CONOPT implementation of the Pindyck model from the GAMS model library. The GAMS model follows as documentation:

!! $Ontext
!!
!! This model finds the optimal pricing and extraction of oil for
!! the opec cartel.
!!
!! Reference: Pindyck R S, Gains to Producers from the
!! Cartelization of Exhaustible Resources, Review of
!! Economics and Statistics, Volume 60, pp 238-251, 1978.
!!
!! $Offtext
!!
!! Sets t       overall time horizon  / 1974*1990 /
!! to(t)   optimization period   / 1975*1990 /
!!
!! Parameter demand(t) equilibrium world demand for fixed prices;
!! demand(to) = 1. + 2.3*1.015**(ord(to)-1);
!!
!! Variables p(t)    world price of oil
!! td(t)   total demand for oil
!! s(t)    supply of oil by non-opec contries
!! cs(t)   cummulative supply by non-opec contries
!! d(t)    demand for opec-oil
!! r(t)    opec reserves
!! rev(t)  revenues in each period
!! profit
!!
!! Positive Variables  p,td,s,cs,d,r
!!
!! Equations tdeq(t) total demand equation
!! seq(t)  supply equation for non-opec contries
!! cseq(t) accounting equation for cummulative supply
!! deq(t)  demand equation for opec
!! req(t)  accounting equation for opec reserves
!! drev(t) yearly objective function value
!! tprofit total objective function;
!!
!! $Double
!!
!! tdeq(t-1)..   td(t)   =e= 0.87*td(t-1) - 0.13*p(t) + demand(t);
!! seq(t-1)..    s(t)    =e= 0.75*s(t-1) + (1.1+0.1*p(t))*1.02**(-cs(t)/7);
!! cseq(t-1)..   cs(t)   =e= cs(t-1) + s(t);
!! deq(to)..     d(to)   =e= td(to) - s(to);
!! req(t-1)..    r(t)    =e= r(t-1) - d(t);
!! drev(to)..    rev(to) =e= d(to)*(p(to)-250/r(to));
!! tprofit..     profit  =e= sum(to,rev(to)*1.05**(1-ord(to)));
!!
!! $Single
!!
!! *
!! *  fixed initial conditions
!! *
!! td.fx('1974') =  18; s.fx ('1974') = 6.5;
!! r.fx ('1974') = 500; cs.fx('1974') = 0.0;
!!
!! td.l(to) = 18; s.l(to)= 7; cs.l(to) = 7*ord(to);
!! d.l (to) = td.l(to)-s.l(to); p.l(to) = 14;
!!
!! Loop(t$to(t), r.l(t) = r.l(t-1)-d.l(t) );
!!
!! Display td.l, s.l, cs.l, d.l, r.l;
!!
!! Model robert / all / 
 Solve robert maximizing profit using nlp;
!!

For more information about the individual callbacks, please have a look at the source code.

Function Documentation

◆ buildModel()

pindyck.PDModelData.buildModel ( self )

adding the variables and constraints to the model

Definition at line 40 of file pindyck.py.

◆ evaluateNonlinearTerm()

pindyck.PDModelData.evaluateNonlinearTerm	(	self,
		x,
		rowno,
		ignerr,
		thread )

callback method for evaluating the nonlinear terms in a given row

Parameters

x	the solution vector that needs to be evaluated.
rowno	the number for the row in which the nonlinear term exists.
ignerr	a boolean to indicate whether the current point is safe or unsafe.
thread	the index of the thread from which this method is being called from.

Returns: the value of the nonlinear terms.

Notes: an error in the evaluation is reported by calling errorInEvaluation()

Definition at line 201 of file pindyck.py.

◆ evaluateNonlinearJacobian()

pindyck.PDModelData.evaluateNonlinearJacobian	(	self,
		x,
		rowno,
		jacnum,
		ignerr,
		thread )

callback method for evaluating the jacobian for the nonlinear terms in a given row