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This work proposes an optimization-based stratification framework for stratified sampling using optimal multi-way decision trees, and introduces reduction procedures for redundant candidate paths and assignment constraints, substantially reducing the optimization problem size.
This work proposes a single-level optimization formulation that incorporates the Karush-Kuhn-Tucker optimality conditions of the lower-level MVO into the upper-level learning problem, and explicitly preserves the budget and short-sale constraints while remaining tractable for standard nonlinear optimization solvers.
In portfolio optimization, a cardinality constraint, which limits the number of assets held, plays a key role in cutting down monitoring and transaction costs. However, the resulting problem is NP-hard and becomes computationally difficult to solve globally as the number of candidate assets grows. Safe screening addres...
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