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Variable Constraints

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June 16, 2015, at 12:44 PM by 45.56.3.184 -
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!! Variable Constraints
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September 30, 2008, at 11:06 AM by 158.35.225.227 -
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Variable constraints may lead to infeasible solutions.  For square problems (n'_var_'=n'_eqn_'), the constraints are generally not needed but may help guide the solver to a feasible solution.  Constraints are particularly advantageous to keep variable values away from strongly non-linear regions of the equation residuals.  Good solvers correctly identify infeasible solutions and terminate.
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Variable constraints may lead to infeasible solutions.  For square problems (n'_var_'=n'_eqn_'), the constraints are generally not needed but may help guide the solver to a feasible solution.  Constraints are particularly advantageous to keep variable values away from strongly non-linear regions of the equation residuals.  Good solvers correctly identify infeasible solutions and terminate with an appropriate message.
September 25, 2008, at 01:08 PM by 158.35.225.230 -
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!! Variable Constraints

Constraints serve to bound a parameter or variable with upper and lower limits.  Variable constraints may be expressed as absolute numbers or functions of parameters or variable initial conditions.  A variable constraint is included in the variable declarations section along with the initial condition.  The constraints less than or equal (<=) or simply less than (<) are considered to be equivalent for numerical solutions.  Likewise, greater than or equal (>=) and greater than (>) are equivalent.

!!! Infeasible Solution

Variable constraints may lead to infeasible solutions.  For square problems (n'_var_'=n'_eqn_'), the constraints are generally not needed but may help guide the solver to a feasible solution.  Constraints are particularly advantageous to keep variable values away from strongly non-linear regions of the equation residuals.  Good solvers correctly identify infeasible solutions and terminate.

!!! Example

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 ! Example model that demonstrates parameter declarations
 Model example
  Parameters
    p1 = 1, >=0, < 2
    p2 <= 5
  End Parameters

  Variables
    v1 = 0, >-1, <1
    v2 = 1, >=p1, <=p5*p1
  End Variables

  Equations
    v1 * v2 = p2
    v1 + v2 = p1
  End Equations
 End Model
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