Optimality Condition for Linear Programming Solutions

In summary, a feasible dictionary with a last row of z = z* + ∑ cjxj describes an optimal solution if and only if all c's are ≤ 0. This is because if all c's are ≤ 0, then increasing any of the variables would either lower or not affect the value of the objective function. However, the opposite direction may not always be true, as shown by a counterexample. A feasible dictionary is a system of equations with basic and non-basic variables, and z* is the constant from the objective equation when non-basic variables are set to zero. These terms are commonly used by Chvatal.
  • #1
Kalinka35
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Homework Statement


A feasible dictionary whose last row reads z = z* + ∑ cjxjdescribes an optimal solution if and only if cj ≤ 0 for all j.
Prove or disprove.

Homework Equations


The Attempt at a Solution


It is clear that if all c's are ≤ 0, then the solution is optimal since increasing any of the variables would either lower or not affect the value of the objective function.
The opposite direction does not seem like it would be true, but I have no idea how to start proving that.
 
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  • #2
What is a "feasible dictionary?" And what is z*?
 
  • #3
A feasible dictionary shows the system of equations in the program with the basic variables on the left hand side and the non-basic variables on the right and it describes a feasible solution to the system.

z* is the constant from the objective equation when you set the non-basic variables equal to zero.

These are the terms that Chvatal uses.
 
  • #4
Actually, I found a counterexample so I think I've got it.
 

Related to Optimality Condition for Linear Programming Solutions

1. What is linear programming proof?

Linear programming proof is a mathematical technique used to prove the optimal solution to a linear programming problem. It involves using mathematical equations and inequalities to model a problem and finding the best possible solution that satisfies all constraints.

2. How is linear programming proof used in real-life applications?

Linear programming proof is commonly used in industries such as manufacturing, transportation, and finance to optimize processes and make decisions. It can be used to determine the most efficient allocation of resources, minimize costs, and maximize profits.

3. What are the key components of a linear programming proof?

The key components of a linear programming proof are the objective function, decision variables, constraints, and the feasible region. The objective function represents the goal or objective to be optimized, while the decision variables are the unknown quantities to be determined. Constraints are the limitations or restrictions that must be satisfied, and the feasible region is the set of all possible solutions that satisfy the constraints.

4. How is linear programming proof different from other optimization techniques?

Linear programming proof is different from other optimization techniques, such as dynamic programming or genetic algorithms, because it specifically deals with linear mathematical models. It is also a deterministic method, meaning it always produces the same solution for a given set of inputs, unlike stochastic methods which may produce different results each time.

5. Are there any limitations to linear programming proof?

Yes, linear programming proof has some limitations. It can only be applied to problems that can be modeled using linear equations and inequalities. It also assumes that the relationships between variables are stable and do not change over time. Additionally, the number of decision variables and constraints can greatly impact the complexity of the problem, making it difficult to solve for larger models.

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