Linear Programming Calculator

Objective Function

Z =x +y

Constraints

Non-negativity constraints (x ≥ 0, y ≥ 0) are automatically included

C1:x +y
C2:x +y

Understanding Linear Programming

Linear Programming (LP) is a mathematical technique for optimizing a linear objective function subject to linear equality and inequality constraints.

Components

  • Decision Variables: What we're solving for (x, y)
  • Objective Function: What we optimize (Z)
  • Constraints: Limitations on resources
  • Feasible Region: Area satisfying all constraints

Graphical Method

  1. Graph all constraints
  2. Identify the feasible region
  3. Find corner points (vertices)
  4. Evaluate objective at each vertex
  5. Select optimal vertex

Corner Point Theorem

If a linear program has an optimal solution, then at least one optimal solution occurs at a corner point (vertex) of the feasible region. This is why we only need to evaluate the objective function at the vertices.

Common Applications

Production Planning

Maximize profit given limited resources and production capacity

Diet Problems

Minimize cost while meeting nutritional requirements

Transportation

Minimize shipping costs while meeting demand

About Linear Programming Calculator - Graphical Method

Solve two-variable linear programming problems graphically. Visualize constraints, feasible region, and find optimal solutions.

Our **Linear Programming Calculator** solves optimization problems with two variables using the graphical method. Enter your objective function and constraints to visualize the feasible region and find optimal vertex solutions. For larger problems, use our Simplex Method Calculator.

The graphical method plots constraints as lines, identifies the feasible region (intersection of all constraint half-planes), and evaluates the objective function at each vertex. The optimal solution always occurs at a corner point.

Perfect for learning linear programming concepts before moving to algebraic methods. See how constraints interact, understand feasibility and boundedness, and visualize optimization in action.

Key Features

Graphical visualization
Vertex enumeration
Objective evaluation
Constraint intersection
Feasibility analysis
Corner point identification

Why Use This Tool?

Visual learning
Concept understanding
Quick 2-variable solutions
Optimization insight
Constraint analysis

Common Use Cases

Education: Learn LP fundamentals visually.

Quick Analysis: Two-variable optimization problems.

Verification: Check simplex results graphically.

Demonstrations: Teaching optimization concepts.

Related Tools

How to Use

1

Enter objective function coefficients

2

Choose maximize or minimize

3

Add constraint equations

4

Select constraint types

5

Click Solve

Frequently Asked Questions

Comments & Feedback

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