Thiele rules are polynomial-time computable on voter interval elections via a standard LP that always has an integral optimum, extending to VCI and LC domains with NP-hardness shown on tree-based generalizations.
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Optimal policies under budget and coverage constraints admit an affine threshold characterization with O(1) integrality gap in the LP relaxation; two algorithms (GLC and RC) are analyzed with performance guarantees that depend on cost homogeneity and constraint bindingness.
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Computing Thiele Rules on Interval Elections and their Generalizations
Thiele rules are polynomial-time computable on voter interval elections via a standard LP that always has an integral optimum, extending to VCI and LC domains with NP-hardness shown on tree-based generalizations.
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Optimal Policy Learning under Budget and Coverage Constraints
Optimal policies under budget and coverage constraints admit an affine threshold characterization with O(1) integrality gap in the LP relaxation; two algorithms (GLC and RC) are analyzed with performance guarantees that depend on cost homogeneity and constraint bindingness.