## What is an optimal solution in linear programming?

Definition: A feasible solution to a linear program is a solution that satisfies all constraints. Definition: An optimal solution to a linear program is the feasible solution with the largest objective function value (for a maximization problem).

## What is an optimal solution in linear programming?

Definition: A feasible solution to a linear program is a solution that satisfies all constraints. Definition: An optimal solution to a linear program is the feasible solution with the largest objective function value (for a maximization problem).

## What are the four requirements of a linear programming problem?

Requirement of Linear Programme Problem (L.P.P) | Operations Research

• (1) Decision Variable and their Relationship:
• (2) Well-Defined Objective Function:
• (3) Presence of Constraints or Restrictions:
• (4) Alternative Courses of Action:
• (5) Non-Negative Restriction:

## What is a decision variable in linear programming?

Decision variables describe the quantities that the decision makers would like to determine. They are the unknowns of a mathematical programming model. Typically we will determine their optimum values with an optimization method. The number of decision variables is n, and is the name of the jth variable.

## What is divisibility in linear programming?

Additivity – the function value is the sum of the contributions of each term. Divisibility – the decision variables can be divided into non-integer values, taking on fractional values. Integer programming techniques can be used if the divisibility assumption does not hold.

## What is linear in linear programming?

Linear programming (LP, also called linear optimization) is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships.

## What are the basic assumptions of linear problem?

Proportionality: The basic assumption underlying the linear programming is that any change in the constraint inequalities will have the proportional change in the objective function.

## What are the characteristics of linear programming?

Characteristics of Linear Programming Linearity – The relationship between two or more variables in the function must be linear. It means that the degree of the variable is one. Finiteness – There should be finite and infinite input and output numbers.

## How do you find the maximum and minimum of a feasible region?

For example, the maximum or minimum value of f(x,y)=ax+by+c over the set of feasible solutions graphed occurs at point A,B,C,D,E or F . When the graph of a system of inequalities forms a region that is closed, the region is said to be bounded.

## Who is the father of linear programming?

George B. Dantzig

## What are the basic concept of linear programming?

Linear programming is a mathematical technique for finding optimal solutions to problems that can be expressed using linear equations and inequalities. If a real-world problem can be represented accurately by the mathematical equations of a linear program, the method will find the best solution to the problem.

## What are linear programming techniques?

Linear programming, mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints. This technique has been useful for guiding quantitative decisions in business planning, in industrial engineering, and—to a lesser extent—in the social and physical sciences.

## What are the two forms of LPP?

3.2 Canonical and Standard forms of LPP : Two forms are dealt with here, the canonical form and the standard form.

## How is linear programming used in real life?

Linear programming is often used when seeking the optimal solution to a problem, given a set of constraints. To find the optimum result, real-life problems are translated into mathematical models to better conceptualize linear inequalities and their constraints.

## How do you identify a feasible region?

The feasible region is the region of the graph containing all the points that satisfy all the inequalities in a system. To graph the feasible region, first graph every inequality in the system. Then find the area where all the graphs overlap. That’s the feasible region.