Linear Programming Calculator With Steps

Enter objective coefficients and all constraints. See each corner point tested with clear math quickly. Compare feasible values and learn each optimization step clearly.

Calculator

Calculate first, or press an export button to calculate and download.

Constraints

# x coefficient y coefficient Operator Right side
1
2
3
4
5
6

Example Data Table

Item Input Meaning
Objective Max Z = 40x + 30y Profit from two products
Constraint 1 2x + y ≤ 100 Machine time limit
Constraint 2 x + y ≤ 80 Labor limit
Constraint 3 x ≤ 40 Maximum product x
Constraint 4 y ≤ 70 Maximum product y
Expected result x = 20, y = 60, Z = 2600 Best feasible corner point

Formula Used

The calculator uses a two variable linear programming model.

Objective: Z = c1x + c2y

Constraints: aix + biy ≤, ≥, or = di

Corner point method: solve pairs of boundary lines, test feasibility, then compare objective values.

How to Use This Calculator

  1. Select maximization or minimization.
  2. Enter coefficients for x and y in the objective.
  3. Enter up to six constraints with signs and limits.
  4. Keep nonnegative bounds checked when quantities cannot be negative.
  5. Press Calculate to see the result and steps.
  6. Use CSV or PDF buttons to save the work.

Understanding Linear Programming

Linear programming is a method for choosing the best value under limits. It works when the goal is linear. It also needs linear restrictions. The calculator uses two decision variables, called x and y. This makes the result easy to view by the corner point method.

Why Corner Points Matter

A feasible region is the area that satisfies every constraint. Its borders come from the constraint lines. When a best answer exists, it appears at a corner point. The tool finds line intersections, checks each point, then tests the objective value. This gives clear steps, not only a final number.

Useful Inputs

Start with an objective function, such as maximize profit or minimize cost. Then enter each restriction. A restriction can be less than, greater than, or equal to a limit. Nonnegative settings keep x and y from going below zero. This is common in production, diet, transport, and resource planning problems.

Reading The Result

The result table lists feasible vertices. It shows the objective value at each point. The best row is chosen from that list. If the problem is a maximization task, the largest value is selected. If it is a minimization task, the smallest value is selected. The steps show how boundaries and intersections are used.

Advanced Uses

You can model many classroom and business cases. Products may use labor, machine hours, and material. A diet plan may control calories, protein, and cost. A transport model may balance demand and capacity. The tool is best for two variable problems. It also supports quick comparison of many possible planning choices. These checks reduce common manual mistakes. Larger models usually need simplex or matrix based solvers.

Checking Accuracy

Use consistent units in every row. Do not mix hours with minutes. Check each inequality sign. A reversed sign can change the feasible region. Round only after the final result when possible. The precision box controls display rounding. It does not change the main method.

Exporting Work

Download the CSV file for spreadsheets. Download the PDF report for sharing. Both exports include the main result and tested vertices. This helps show work in assignments or reports. Keep the entered data with your notes. It makes review easier later.

FAQs

What is linear programming?

Linear programming finds the best value of a linear objective under linear constraints. It is used for profit, cost, resource, and planning problems.

How many variables does this calculator support?

It supports two variables, x and y. This allows the calculator to use the graphical corner point method and show clear steps.

Can it solve minimization problems?

Yes. Select minimization. The calculator evaluates every feasible vertex and picks the smallest objective value.

What are feasible vertices?

Feasible vertices are corner points that satisfy every constraint. They are tested because a finite linear programming optimum occurs at a feasible corner.

Why should I use nonnegative bounds?

Many real quantities cannot be negative. Products, hours, materials, and costs usually need x ≥ 0 and y ≥ 0.

Can I enter greater than constraints?

Yes. Each constraint row supports less than, greater than, and equal signs. Choose the sign that matches your model.

What happens if no feasible point is found?

The constraints may conflict, or a sign may be entered incorrectly. Review each row, then check the variable bounds.

What is included in the exports?

The CSV and PDF reports include the objective, constraints, status, feasible vertices, and optimal result when available.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.