Constrained Optimization Quant Interview Guide
Constrained optimization quant interview guide for objectives, equality constraints, inequality constraints, feasible sets, Lagrange intuition, and examples.
Candidates discussing portfolio weights, risk limits, calibration, and feasibility tradeoffs.
Constraints define feasible choices
A constrained optimization problem chooses the best feasible point, not the unconstrained best point. Constraints can be equalities, inequalities, bounds, or business rules.
Lagrange intuition prices constraints
In smooth equality-constrained problems, Lagrange multipliers can be interpreted as sensitivity to relaxing a constraint, subject to assumptions.
Concrete example
A portfolio may optimize expected utility while requiring weights to sum to one, sector exposures to stay bounded, and turnover to remain below a limit.
Feasibility is not guaranteed
Too many constraints can make the feasible set empty or force unstable corner solutions. Check feasibility before trusting the optimizer output.
Common mistakes
Candidates often solve the unconstrained problem and add limits later. Strong answers include constraints from the start and discuss sensitivity.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.