Examples
Examples
Try the starting example
- Objective coefficients for x1,x2,…
- 3,2
- Constraints: space-separated coefficients, <= or >= or =, right side; or none
- 1 1,<=,4 1 0,<=,2 0 1,<=,3
- Nonnegative lower bounds
- 0,0
- Upper bounds, comma-separated; none means unbounded
- none,none
- Direction: maximize or minimize
- maximize
- Solver status
- optimal
- Optimal objective when available
- 10
- Optimal variable values x1,x2,…
- 2, 2
- Maximum returned constraint violation
- 0
Try another scenario
- Objective coefficients for x1,x2,…
- 1
- Constraints: space-separated coefficients, <= or >= or =, right side; or none
- 1,>=,1 1,<=,0
- Nonnegative lower bounds
- 0
- Upper bounds, comma-separated; none means unbounded
- none
- Direction: maximize or minimize
- maximize
- Solver status
- infeasible
- Optimal objective when available
- Not applicable
- Optimal variable values x1,x2,…
- Not applicable
- Maximum returned constraint violation
- Not applicable
Calculation method
Calculation method
Solver status
Solve a continuous linear program with up to ten nonnegative variables, thirty explicit rows and user-supplied bounds. Report optimal, infeasible, unbounded or solver-limit status.
Optimal objective when available
Return the solver objective only for an optimal result.
Optimal variable values x1,x2,…
Fill omitted zero variables with zero and preserve coefficient order.
Maximum returned constraint violation
Independently substitute the returned variables into all supplied rows and bounds; reject a candidate exceeding the residual tolerance.
Symbols and units
- a
- Objective coefficients for x1,x2,…
- b
- Constraints: space-separated coefficients, <= or >= or =, right side; or none
- c
- Nonnegative lower bounds
- d
- Upper bounds, comma-separated; none means unbounded
- e
- Direction: maximize or minimize
Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.
Assumptions & limits
Assumptions & limits
- A bounded-size continuous LP, not integer or nonlinear optimisation. Variables have explicit nonnegative lower bounds; upper bounds may be none. Numerical optimality depends on solver precision and conditioning. Double-precision results are approximate. Numeric tables use 12 significant digits and display components at roundoff scale (32 machine epsilons relative to their row or relevant calculation scale) as zero; scalar diagnostics retain full precision.
Are these inputs private?
These calculations run in your browser. A shared link contains the values you choose to share. Only questions submitted to the separate AI finder are sent to OpenAI.
Further reading
YALPS: linear-program model and solver statuses ↗
Background reading; this does not imply endorsement or independent review of SumFizz.