Exact geometric characterization of optimality-preserving LP compressions together with a tractable learning algorithm achieving 1/n-rate recovery guarantees on unseen instances.
Yonatan Bilu and Nathan Linial
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proves NP-hardness of computing decision-relevant dimension d* and coNP-hardness of global sufficiency in linear optimization, then gives poly-time pointwise algorithms, a cumulative compression scheme of size at most d*, and PAC bounds scaling with d* for contextual linear optimization.
citing papers explorer
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The Geometry of Linear Program Compression: An Exact Characterization and Learning Algorithm
Exact geometric characterization of optimality-preserving LP compressions together with a tractable learning algorithm achieving 1/n-rate recovery guarantees on unseen instances.
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Learning Decision-Sufficient Representations for Linear Optimization
Proves NP-hardness of computing decision-relevant dimension d* and coNP-hardness of global sufficiency in linear optimization, then gives poly-time pointwise algorithms, a cumulative compression scheme of size at most d*, and PAC bounds scaling with d* for contextual linear optimization.