Benoît Legat

Practical 5 – Sensitivity analysis of a linear program

Inspired from a JuMP tutorial

In this practical session, we explain the sensitivity interpretation of dual values using the lp_sensitivity_report function.

In brief, sensitivity analysis of a linear program is about asking two questions:

  1. Given an optimal solution, how much can I change the objective coefficients before a different solution becomes optimal?

  2. Given an optimal solution, how much can I change the right-hand side of a linear constraint before a different solution becomes optimal?

JuMP provides a function, lp_sensitivity_report, to help us compute these values, but this tutorial extends that to create more informative tables in the form of a DataFrame.


This tutorial uses the following packages:

using JuMP
import HiGHS
import DataFrames

as well as this small linear program:

model = Model(HiGHS.Optimizer)
@variable(model, x >= 0)
@variable(model, 0 <= y <= 3)
@variable(model, z <= 1)
@objective(model, Min, 12x + 20y - z)
@constraint(model, c1, 6x + 8y >= 100)
@constraint(model, c2, 7x + 12y >= 120)
@constraint(model, c3, x + y <= 20)
solution_summary(model; verbose = true)

Can you identify:

Sensitivity reports

Now let's call lp_sensitivity_report:

report = lp_sensitivity_report(model)

It returns a SensitivityReport object, which maps:

Both tuples are relative, rather than absolute. So, given an objective coefficient of 1.0 and a tuple (-0.5, 0.5), the objective coefficient can range between 1.0 - 0.5 an 1.0 + 0.5.

For example:


indicates that the objective coefficient on x, that is, 12, can decrease by -0.333 or increase by 3.0 and the primal solution (15, 1.25) will remain optimal. In addition:


means that the right-hand side of the c1 constraint (100), can decrease by 4 units, or increase by 2.85 units, aand the primal solution (15, 1.25) will remain optimal.

Variable sensitivity

By themselves, the tuples aren't informative. Let's put them in context by collating a range of other information about a variable:

function variable_report(xi)
    return (
        name = name(xi),
        lower_bound = has_lower_bound(xi) ? lower_bound(xi) : -Inf,
        value = value(xi),
        upper_bound = has_upper_bound(xi) ? upper_bound(xi) : Inf,
        reduced_cost = reduced_cost(xi),
        obj_coefficient = coefficient(objective_function(model), xi),
        allowed_decrease = report[xi][1],
        allowed_increase = report[xi][2],

Calling our function on x:

x_report = variable_report(x)

That's a bit hard to read, so let's call this on every variable in the model and put things into a DataFrame:

variable_df =
    DataFrames.DataFrame(variable_report(xi) for xi in all_variables(model))

Great! That looks just like the reports in Excel.

Constraint sensitivity

We can do something similar with constraints:

function constraint_report(ci)
    return (
        name = name(ci),
        value = value(ci),
        rhs = normalized_rhs(ci),
        slack = normalized_rhs(ci) - value(ci),
        shadow_price = shadow_price(ci),
        allowed_decrease = report[ci][1],
        allowed_increase = report[ci][2],

c1_report = constraint_report(c1)

That's a bit hard to read, so let's call this on every variable in the model and put things into a DataFrame:

constraint_df = DataFrames.DataFrame(
    constraint_report(ci) for (F, S) in list_of_constraint_types(model) for
    ci in all_constraints(model, F, S) if F == AffExpr

Analysis questions

Now we can use these dataframes to ask questions of the solution.

For example, we can find basic variables by looking for variables with a reduced cost of 0:

basic = filter(row -> iszero(row.reduced_cost), variable_df)

and non-basic variables by looking for non-zero reduced costs:

non_basic = filter(row -> !iszero(row.reduced_cost), variable_df)

we can also find constraints that are binding by looking for zero slacks:

binding = filter(row -> iszero(row.slack), constraint_df)

or non-zero shadow prices:

binding2 = filter(row -> !iszero(row.shadow_price), constraint_df)

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