Solve Linear Programing Calculator

Enter coefficients and compare feasible corner points. Check objective value, slack, surplus, feasibility, and status. Export clean reports after solving each model with confidence.

Calculator Inputs

Constraints

Example Data Table

Itemx coefficienty coefficientSignRight side
Objective profit4030MaxZ
Machine hours21100
Labor hours1180
Product X cap1040

Formula Used

The objective is Z = c1x + c2y. The calculator maximizes or minimizes this value.

Each constraint has the form aix + biy ≤, ≥, or = ri.

For two boundary lines, the determinant is D = a1b2 − a2b1.

If D is not zero, x = (r1b2 − r2b1) / D.

Also, y = (a1r2 − a2r1) / D.

Slack for ≤ is ri − LHS. Surplus for ≥ is LHS − ri.

How To Use This Calculator

  1. Choose maximize or minimize.
  2. Enter the objective coefficients for x and y.
  3. Activate the constraints needed for your model.
  4. Enter each constraint coefficient, sign, and right side value.
  5. Keep nonnegative variables checked unless your model allows negatives.
  6. Press the submit button to review the result above the form.
  7. Use the CSV or PDF option to save the report.

What This Calculator Solves

A linear program searches for the best value of a linear objective. It uses straight line constraints. This calculator handles two decision variables, named x and y. You can maximize profit, minimize cost, or test a planning model. The tool checks every feasible corner point. That method works because an optimal finite answer occurs at a vertex for this type of model.

Why Vertex Testing Matters

Each constraint draws a boundary line. The allowed side forms the feasible region. When several limits overlap, they create corners. The calculator intersects boundary pairs, filters infeasible points, and evaluates the objective at each remaining point. It also checks axes when nonnegative variables are required. This helps you see why the selected point is optimal.

Planning Uses

Students can test homework models quickly. Analysts can compare production mixes, labor limits, diet targets, shipping plans, and budget allocations. The calculator is also helpful for teaching. It lists candidate points, objective values, slack, surplus, and binding constraints. These details make the result easier to audit.

Understanding Slack

Slack shows unused capacity for a less than or equal constraint. Surplus shows how far a greater than or equal target is exceeded. A binding constraint has almost no slack or surplus. Binding limits often explain the final plan. They are the boundaries that shape the chosen corner.

Good Input Practice

Use consistent units before solving. Keep coefficients in the same scale. For example, do not mix minutes and hours in one labor constraint. Enter negative coefficients only when the model truly needs them. Review every inequality sign carefully. A reversed sign can change the answer completely.

Result Limits

This calculator is designed for two variables. Larger linear programs need matrix methods such as simplex or interior point algorithms. Still, two variable models are powerful. They show the geometry behind optimization. They also help users learn how constraints, objective direction, and feasibility connect.

Final Advice

Start with a simple example. Confirm the feasible region. Then add real numbers. Download the CSV or PDF report after solving. The exported report helps document assumptions, formulas, and final values for review.

Save the scenario name, because it keeps reports easy to trace during later model revisions and audits.

FAQs

What does this calculator solve?

It solves two variable linear programming models by checking feasible corner points and comparing objective values.

Can it maximize and minimize?

Yes. Choose maximize for profit or output goals. Choose minimize for cost, waste, time, or resource goals.

How many constraints can I enter?

The form includes six optional constraints. You can activate only the rows needed for your current model.

What are binding constraints?

Binding constraints have no meaningful slack or surplus at the selected point. They usually shape the optimal solution.

What does unbounded mean?

Unbounded means the objective can keep improving along a feasible direction. A finite optimal answer does not exist.

Should x and y be nonnegative?

Most planning models require nonnegative decisions. Keep the option checked unless negative variable values make sense.

Why are vertices important?

For a bounded two variable linear model, the best value occurs at a feasible vertex or along an edge.

Can I export the answer?

Yes. After solving, use the CSV or PDF buttons to download a report with the result and tables.

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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.