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Depending on the nature of <math>f(x,\theta)</math> and <math>g(x,\theta)</math> and whether the optimization problem features integer variables, parametric programming problems are classified into different sub-classes:
* If more than one parameter is present, i.e. <math>m > 1</math>, then it is often referred to as multiparametric programming problem<ref>{{cite journal|last1=Gal|first1=Tomas|last2=Nedoma|first2=Josef|title=Multiparametric Linear Programming|journal=Management Science|date=1972|volume=18|issue=7|pages=406–422|jstor=2629358|doi=10.1287/mnsc.18.7.406}}</ref>
* If integer variables are present, then the problem is referred to as (multi)parametric mixed-integer programming problem<ref>{{cite journal|last1=Dua|first1=Vivek|last2=Pistikopoulos|first2=Efstratios N.|title=Algorithms for the Solution of Multiparametric Mixed-Integer Nonlinear Optimization Problems|journal=Industrial & Engineering Chemistry Research|date=October 1999|volume=38|issue=10|pages=3976–3987|doi=10.1021/ie980792u}}</ref>
* If constraints are [[Affine transformation|affine]], then additional classifications depending to nature of the objective function in (multi)parametric (mixed-integer) linear, quadratic and nonlinear programming problems is performed. Note that this generally assumes the constraints to be affine.<ref>{{cite book|last1=Pistikopoulos |first1=Efstratios N. |last2=Georgiadis |first2=Michael C. |last3=Dua |first3=Vivek |date=2007 |title=Multi-parametric Programming Theory, Algorithms and Applications |publisher=Wiley-VCH |___location=Weinheim |doi=10.1002/9783527631216 |isbn=9783527316915 }}</ref>
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