As we have discussed in previous articles, the Shadow Price of a constraint represents the rate of change of the optimal value as a result of a marginal change in the right-hand side of a constraint. “Marginal” is understood as a modification that does not change the geometry of the problem, that is to say, […]

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## What is a Degenerate Optimal Solution in Linear Programming

When applying the Simplex Method to calculate the minimum coefficient or feasibility condition, if there is a tie for the minimum ratio or minimum coefficient it can be broken arbitrarily. In this instance, at least one basic variable will become zero in the following iteration, confirming that in this instance the new solution is degenerate. […]

## How to Calculate the Shadow Price of a Constraint

The Shadow Price of a constraint in Linear Programming indicates how much the value of an objective (optimal) function changes due to a marginal variation in the right-hand side of a constraint. It is assumed that the rest of the model’s parameters remain constant. It should be noted beforehand that the Shadow Price can be […]

## Changes in the Right Hand Side (RHS) of the Constraint (Sensitivity Analysis in Linear Programming)

Vector on Right Hand Side (RHS) associated with the constrains of a Linear Programming model may have different practical interpretations such as the availability of inputs for the manufacture of certain products, limiting of capacity, demand requirements, among other. As a result it is interesting to analyze the impact of the change of one or more […]

## What is a Basic Feasible Solution in Linear Programming

In Linear Programming (LP) a basic feasible solution is one that also belong to the feasible region or problem area can be represented by a feasible solution in implementing the Simplex Method satisfying nonnegative conditions. In this context, a basic solution corresponds to one of the vertices whose coordinate feasibility domain or solution can be represented by a set of […]