semidefinite programming
PulseAugur coverage of semidefinite programming — every cluster mentioning semidefinite programming across labs, papers, and developer communities, ranked by signal.
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Deep neural network framework simplifies AC power flow contingency analysis
Researchers have developed a novel framework for AC power flow contingency analysis that utilizes a single deep neural network (DNN) to predict post-contingency operating states. This approach aims to reduce the computa…
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New SSLD method enhances graph coloring heuristic with SDP preprocessing
Researchers have developed a new method called SSLD (Semidefinite Spectral Learning with DSATUR) that enhances the DSATUR heuristic for the NP-hard Graph Coloring Problem. SSLD preprocesses a graph by identifying a high…
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Semidefinite Programming applied to Quantum Channel Learning
A new paper details the application of Semidefinite Programming (SDP) to the problem of reconstructing quantum channels from classical data. The research, led by Vladislav Malyshkin, highlights that SDP can efficiently …
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New $(\text{DNN})^2$ method enhances neural network verification
Researchers have developed a new method called $(\text{DNN})^2$ to improve the verification of deep neural networks, particularly those using rectified linear units (ReLUs). Existing methods often provide overly conserv…
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Formalizing Flag Algebras in Lean for Graph Theory Proofs
Researchers have developed a machine-checked formalization of Razborov's flag algebra method within the Lean proof assistant. This formalization enables a compiler that transforms semidefinite programming output into ve…
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New framework analyzes MAXCUT-based clustering algorithms with theoretical guarantees
This paper introduces a new framework for analyzing three algorithms—SDP1, BalancedSDP, and Spectral clustering—used for partitioning data samples drawn from mixtures of two sub-Gaussian distributions. The researchers p…
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New semidefinite programming approach for mixture models in machine learning
A new research paper introduces a semidefinite programming approach to approximate target measures using mixtures of distributions, such as Gaussian mixture models. This method is particularly useful for determining mix…
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Neural networks predict quantum material properties with high accuracy
Researchers have developed a new neural network framework designed to predict two-particle reduced density matrices (2-RDMs) with improved accuracy and efficiency. This framework incorporates representability conditions…
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New randomized algorithm tackles NP-hard Sparse PCA
Researchers have developed a new randomized approximation algorithm for Sparse Principal Component Analysis (SPCA), a technique crucial for dimensionality reduction that is known to be NP-hard. The algorithm leverages a…
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New SDP framework enables AI agents to build state spaces
Researchers have developed a new framework called the State-Centric Decision Process (SDP) to address limitations in language environments for AI agents. SDP enables agents to construct necessary inputs like state space…
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New methods enhance robust optimization with ensemble models and worst-case distribution analysis
Researchers have developed new methods for distributionally robust optimization, a technique that accounts for uncertainty in data distributions. One approach, Ensemble Distributionally Robust Bayesian Optimization, use…
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Researchers achieve near-optimal regret in safe learning-based control for constrained LQR
Researchers have developed a new algorithm for adaptive control of stochastic linear quadratic regulators with constraints. This algorithm achieves near-optimal regret of $\tilde{O}(\sqrt{T})$ and satisfies chance const…