Linear Programming Optimal Solution Calculator

Find optimal values from constraints and objectives. Compare feasible corner points using simplex style checks. Download clean reports for classroom and planning work today.

Calculator Form

Constraints

Example Data Table

Item Expression Limit Meaning
Objective Max Z = 40x + 30y Best value Profit from two products
Constraint 1 2x + y ≤ 100 100 Material capacity
Constraint 2 x + y ≤ 80 80 Demand capacity
Constraint 3 x + 3y ≤ 90 90 Labor capacity

Formula Used

Objective function: Z = c1x + c2y.

Constraints: aix + biy ≤, ≥, or = ri.

Intersection of two boundary lines: x = (r1b2 - r2b1) / (a1b2 - a2b1).

Y value: y = (a1r2 - a2r1) / (a1b2 - a2b1).

The calculator tests every corner point. The highest or lowest objective value becomes the optimal solution.

How To Use This Calculator

Choose maximize or minimize. Enter the objective coefficients for X and Y. Add labels if needed. Type each constraint coefficient, sign, and right side value.

Keep the nonnegative option selected when X and Y cannot be below zero. Leave unused constraint rows blank. Press the calculate button. Read the optimal point, objective value, feasible points, and slack table.

Use the CSV button for spreadsheet records. Use the PDF button for a simple report file.

Linear Programming Optimal Solution Guide

Linear programming helps choose the best result under limits. It is used in production, transport, finance, diet planning, staffing, and scheduling. The model has an objective function. It also has constraints. Each constraint defines what is allowed.

Why It Matters

A manager may want maximum profit. A planner may want minimum cost. A student may need each feasible corner point. This calculator focuses on two decision variables. That format is common in classroom work. It is also useful for quick business checks.

How The Method Works

The tool reads the coefficients for X and Y. It then reads each inequality. Boundary lines are formed from every active constraint. The program finds intersections between those lines. It also checks the axes. Each point is tested against every rule. Only feasible points remain.

The objective value is calculated at each feasible point. For a maximum problem, the largest value is selected. For a minimum problem, the smallest value is selected. This follows a key linear programming idea. When an optimum exists in a bounded polygon, it occurs at a corner point.

Practical Use

You can enter material limits, labor limits, demand limits, budget limits, or capacity limits. Use less than, greater than, or equal signs as needed. Leave unused rows blank. The result table shows every feasible corner. That helps compare choices and explain the answer.

Reading The Result

The best X and Y values show the decision plan. The objective value shows profit, cost, score, or another target. Binding constraints have little or no slack. Nonbinding constraints still have unused capacity. Check slack before changing a plan.

Important Notes

This calculator solves two variable linear programs through corner point enumeration. It is designed for transparent learning. Large industrial problems may need matrix solvers. Still, this page gives a clear first answer and downloadable records. Use the CSV file for spreadsheets. Use the document export for reports.

The example table shows one balanced product mix. You can replace those values with your own problem. Keep units consistent. For example, do not mix hours with minutes unless converted. Better input quality creates stronger decisions, cleaner explanations, and fewer mistakes.

Review each result before using it for final planning.

FAQs

What does this calculator solve?

It solves two variable linear programming problems. It finds feasible corner points and selects the best objective value for maximization or minimization.

Can I use greater than constraints?

Yes. Choose the greater than sign in the constraint row. The calculator will test feasibility using that selected direction.

What is a feasible point?

A feasible point satisfies every active constraint. It also satisfies nonnegative rules when that option is selected.

Why are corner points important?

For a bounded two variable linear program, the optimal value occurs at a feasible corner point. Testing corners gives a clear solution.

What does slack mean?

Slack shows unused capacity for a less than constraint. For a greater than constraint, it shows surplus above the required level.

Can the answer be unbounded?

Yes. If the feasible region continues forever in an improving direction, a finite optimum may not exist. Add a practical limit.

Can I download results?

Yes. After calculation, use the CSV or PDF buttons. They export the objective, constraints, result, and feasible points.

How many variables are supported?

This page supports two decision variables. That keeps the corner point method transparent and easy to verify by hand.

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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.