A constrained optimization method is introduced to enforce exact first- and second-moment recovery in low-order polynomial chaos expansions, demonstrated on algebraic test functions.
General heuristics for nonconvex quadratically con- strained quadratic programming
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abstract
We introduce the Suggest-and-Improve framework for general nonconvex quadratically constrained quadratic programs (QCQPs). Using this framework, we generalize a number of known methods and provide heuristics to get approximate solutions to QCQPs for which no specialized methods are available. We also introduce an open-source Python package QCQP, which implements the heuristics discussed in the paper.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
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On Improved Statistical Accuracy of Low-Order Polynomial Chaos Approximations
A constrained optimization method is introduced to enforce exact first- and second-moment recovery in low-order polynomial chaos expansions, demonstrated on algebraic test functions.
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Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.