Feasible region
A feasible region is the set of points or variable assignments that satisfy all constraints of an optimization problem.
Build a Robo-Advisor with Python (From Scratch)
A feasible set is the set of variable values that satisfy all constraints of an optimization problem.
- How it is used
- Defines the constraint-satisfying search space for portfolio optimization.
- How deep it goes
- Explains endpoints, convexity, iterative search obstruction, and an empty constrained portfolio case.
- Examples
- Uses bounded one-variable objectives and capped VTI, VEA, VWO, AGG, BNDX, and EMB portfolio weights.
- What you can do after
- Diagnose/debug with it by recognizing when combined portfolio constraints leave no admissible allocation.
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Bayesian Optimization in Action
A feasible region is the subset of the search space containing inputs that satisfy all specified optimization constraints.
- How it is used
- Guides black-box optimization toward candidates that improve the objective without violating constraints.
- How deep it goes
- Defines feasibility, models unknown constraints with Gaussian processes, and stops short of deriving the acquisition policy mathematically.
- Examples
- Uses a bounded one-dimensional objective, a nonpositive cost constraint, Gaussian-process models, and constrained expected improvement.
- What you can do after
- Apply constrained optimization via BoTorch's ConstrainedExpectedImprovement acquisition function.
Read how Bayesian Optimization in Action explains feasible region →
Related concepts
- constraintproduced by · A constraint is a condition or rule that restricts the permissible states, values, actions, or solutions in a system or problem.
- search spacecontrasts with · A search space is the set of possible states, choices, configurations, or candidate solutions that may be considered when exploring or solving a problem.
- feasible solutionhas part · A feasible solution is an assignment of decision-variable values that satisfies all specified constraints and bounds.
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