Exponential lower bounds for cutting planes and Res(⊕) on binary clique formulas for random dense graphs, with polynomial randomized communication complexity for falsified clause finding.
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Superposition relaxation creates separable estimators for factorable functions that are tighter than McCormick relaxations in numerical tests while providing convergence guarantees.
SILAS jointly optimizes polynomial ODE vector fields and polynomial Lyapunov functions from data to produce models with provably bounded trajectories via compact absorbing sets.
Deleting k colors to place the residual augmented graph in a uniformly rank-r exact hereditary class yields Lasserre exactness at level k+r, with color-intersection graphs inducing clique-sum locality for blockwise algorithms on rainbow matching.
A DRL-trained unrolled QP network serves as a model-free safety filter with formal persistent safety guarantees.
A certificate of unboundedness is introduced for arbitrary polynomial optimization problems to detect cases with no finite lower bound.
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Exponential lower bounds for cutting planes and Res(⊕) on binary clique formulas for random dense graphs, with polynomial randomized communication complexity for falsified clause finding.
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Superposition relaxation creates separable estimators for factorable functions that are tighter than McCormick relaxations in numerical tests while providing convergence guarantees.
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SILAS jointly optimizes polynomial ODE vector fields and polynomial Lyapunov functions from data to produce models with provably bounded trajectories via compact absorbing sets.
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Grouped Color Deletion, Lasserre Exactness and Clique-Sum Locality for Rainbow Matching
Deleting k colors to place the residual augmented graph in a uniformly rank-r exact hereditary class yields Lasserre exactness at level k+r, with color-intersection graphs inducing clique-sum locality for blockwise algorithms on rainbow matching.
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A Certificate of Unboundedness for Polynomial Optimization Problems
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