Feasible solution
A feasible solution is an assignment of decision-variable values that satisfies all specified constraints and bounds.
Bayesian Optimization in Action
A feasible point or solution is a candidate assignment that satisfies all constraints in a constrained optimization problem.
- How it is used
- Uses constraint satisfaction to restrict optimization to practically acceptable candidate solutions.
- How deep it goes
- Defines feasible and infeasible points, illustrates changed optima, and extends feasibility to probabilistic Gaussian-process estimates.
- Examples
- Uses a constrained Forrester function and an airplane-design problem with a simulated nonlinear cost function.
- What you can do after
- Evaluate candidate optima by checking constraints and select the highest-valued feasible point.
Read how Bayesian Optimization in Action explains feasible solution →
Optimization Algorithms: AI techniques for design, planning, and control problems
A feasible solution is a candidate solution that satisfies all specified constraints.
- How it is used
- Frames optimization as finding candidates that satisfy constraints before comparing their objective-function values.
- How deep it goes
- Defines feasibility and contrasts it with optimality, near-optimality, and search, without developing an implementation procedure.
- Examples
- Uses a conference ticket-price problem with demand, costs, profit, and price bounds.
- What you can do after
- Evaluate candidate solutions by checking constraint satisfaction separately from objective-function quality.
Related concepts
- infeasible pointcontrasts with · An infeasible point is a candidate input that violates at least one constraint and therefore cannot be accepted as a solution.
- optimal solutionincludes · An optimal solution is a feasible solution that achieves the best objective value among the alternatives considered.
- feasible regionpart of · A feasible region is the set of points or variable assignments that satisfy all constraints of an optimization problem.
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