For readers new to optimization:Documentation Index
Fetch the complete documentation index at: https://feasible-1447f9c5.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
- Constraint. A rule the answer must obey. Example: “no two queens in the same row.”
- Decision variable. A quantity the optimizer chooses. Often binary (0/1) for yes-no decisions.
- Feasible. A solution that satisfies all constraints. (Also the name of this product.)
- Infeasible. No solution exists. The constraints contradict each other, or the data forces an impossible outcome.
- Linear program (LP). All constraints and objective are linear in the variables. Easy to solve.
- MIP / MILP. Mixed-Integer (Linear) Program. Some variables are required to be integers — much harder than LP, but covers many real problems.
- MINLP. Mixed-Integer Nonlinear Program. Hardest of the common classes. Solvers exist but convergence isn’t guaranteed.
- Objective. The thing you want to minimize or maximize. Cost, time, satisfaction, profit.
- Optimal. A feasible solution with the best objective value. There can be multiple optimal solutions.
- Relaxation. Drop some constraints (e.g., integrality) to get an easier problem whose solution bounds the original.
- Solver. The piece of software that takes a formulated problem and finds a solution.
- Standard form. The mathematical statement of a problem: variables, constraints, objective. Feasible builds this for you from natural language.