Function GMP::Instance::CreateFeasibility(GMP, name, useMinMax)

# GMP::Instance::CreateFeasibility

The function GMP::Instance::CreateFeasibility creates a mathematical program that is the feasibility problem of a generated mathematical program. Its main purpose is to identify infeasibilities in an infeasible problem. The feasibility problem can be used to minimize the sum of infeasibilities, or to minimize the maximum infeasibility.

This function can be used for both linear and nonlinear problems but not for constraint programming problems.
GMP::Instance::CreateFeasibility(
GMP,            ! (input) a generated mathematical program
[name],         ! (input, optional) a string expression
[useMinMax]     ! (input, optional) integer, default 0
)


## Arguments

GMP

An element in the set AllGeneratedMathematicalPrograms.

name

A string that contains the name for the feasibility problem.

useMinMax

If 0, the sum of infeasibilities will be minimized, else the maximum infeasibility will be minimized.

### Mathematical formulation

In this section we show how the feasibility problem is constructed. To simplify the explanation we use a linear problem but the same construction applies to a nonlinear problem.

Consider the following problem where $$J$$ denotes the set of variables, $$I_1$$ the set of $$\geq$$ inequalities, $$I_2$$ the set of $$\leq$$ inequalities, and $$I_3$$ the set of equalities.
\begin{split}\begin{aligned} \max \quad & \sum_{j\in J} a_{j} x_j \\ \text{s.t.} \quad & \sum_{j\in J} a_{ij} x_j && \geq b_i \quad i \in I_1 \\ & \sum_{j\in J} a_{ij} x_j && \leq b_i \quad i \in I_2 \\ & \sum_{j\in J} a_{ij} x_j && = b_i \quad i \in I_3 \\ & x \geq 0 \end{aligned}\end{split}

Then if we minimize the sum of infeasibilities the feasibility problem becomes:

\begin{split}\begin{aligned} \min \quad & \sum_{i \in I_1} z^p_i + \sum_{i \in I_2} z^n_i + \\ & \sum_{i \in I_3} (z^p_i + z^n_i) \\ \text{s.t.} \quad & \sum_{j\in J} a_{ij} x_j + z^p_i && \geq b_i \quad && i \in I_1 \\ & \sum_{j\in J} a_{ij} x_j - z^n_i && \leq b_i \quad && i \in I_2 \\ & \sum_{j\in J} a_{ij} x_j + z^p_i - z^n_i && = b_i \quad && i \in I_3 \\ & x, z^p, z^n \geq 0 \end{aligned}\end{split}

If we minimize the maximum infeasibility the feasibility problem becomes:

\begin{split}\begin{aligned} \min \quad & z^m \\ \text{s.t.} \quad & \sum_{j\in J} a_{ij} x_j + z^m && \geq b_i \quad && i \in I_1 \\ & \sum_{j\in J} a_{ij} x_j - z^m && \leq b_i \quad && i \in I_2 \\ & \sum_{j\in J} a_{ij} x_j + z^p_i - z^n_i && = b_i \quad && i \in I_3 \\ & z^m - z^p_i - z^n_i && \geq 0 \quad && i \in I_3 \\ & x, z^p, z^n \geq 0 \end{aligned}\end{split}

## Return Value

A new element in the set AllGeneratedMathematicalPrograms with the name as specified by the name argument.

Note

• The name argument should be different from the name of the original generated mathematical program.

• If the name argument is not specified then AIMMS will name the generated math program as “Feasibility problem of” followed by the name of the GMP.

• If an element with name specified by the name argument is already present in the set AllGeneratedMathematicalPrograms the corresponding generated mathematical program will be replaced (or updated in case the same symbolic mathematical program is involved).

• By using the suffices .ExtendedVariable and .ExtendedConstraint it is possible to refer to the columns and rows that are added to create the feasibility problem. In case the sum of infeasibilities is minimized only variables are added:

• The variable c.ExtendedVariable('PositiveViolation',i) is added for a constraint c(i) with type $$\geq$$.

• The variable c.ExtendedVariable('NegativeViolation',i) is added for a constraint c(i) with type $$\leq$$.

• The variables c.ExtendedVariable('PositiveViolation',i) and c.ExtendedVariable('NegativeViolation',i) are added for an equality constraint c(i).

In case the maximum infeasibility is minimized the following variables and constraints are added:

• The variable mp.ExtendedVariable('MaximumViolation') is added for math program mp.

• The variables c.ExtendedVariable('PositiveViolation',i) and c.ExtendedVariable('NegativeViolation',i) are added for an equality constraint c(i).

• The constraint c.ExtendedConstraint('MaximumViolation',i) is added for an equality constraint c(i).

In the above mathematical formulation,

• c.ExtendedVariable('PositiveViolation',i) corresponds to $$z^p_i$$.

• c.ExtendedVariable('NegativeViolation',i) corresponds to $$z^n_i$$.

• mp.ExtendedVariable('MaximumViolation') corresponds to $$z^m$$.