In the context of linear programming and optimization, the shadow price (also known as the dual value) is a concept that provides valuable information about the rate of change in the optimal objective function value with respect to a one-unit change in the right-hand side of a constraint, while keeping all other variables and constraints constant. Shadow prices are associated with the constraints of an optimization problem and play a crucial role in sensitivity analysis, which helps us understand how changes in the constraints impact the optimal solution and objective function value.

Shadow price, also known as dual value, is a concept used in linear programming and optimization to measure the rate of change in the optimal objective function value with respect to changes in the constraints' coefficients.

The right-hand side (RHS) of the constraints is connected to Shadow Prices. The core variables and their respective bounds are linked to Reduced Costs.

A shadow price is a monetary value attributed to a non-marketable, abstract, or intangible good or service. Because there aren't enough solid data points to base calculations on, it frequently relies on a variety of assumptions and guesses, making it arbitrary and imperfect.