Optimization
Solving for the portfolio weights that maximize an objective (e.g., return or utility) subject to constraints (e.g., risk or budget).
The classic Markowitz optimization traces the efficient frontier by minimizing variance for each target return. Because expected-return estimates carry the most uncertainty and the most influence, real-world optimizers add constraints, shrinkage, or resampling to produce stable, investable portfolios.
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