SOS-SDP: An Exact Solver for Minimum Sum-of-Squares Clustering (Q5106412)

scientific article; zbMATH DE number 7587562

Language	Label	Description	Also known as
English	SOS-SDP: An Exact Solver for Minimum Sum-of-Squares Clustering	scientific article; zbMATH DE number 7587562

Statements

instance of

scholarly article

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title

SOS-SDP: An Exact Solver for Minimum Sum-of-Squares Clustering (English)

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INFORMS Journal on Computing

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publication date

19 September 2022

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full work available at URL

https://arxiv.org/abs/2104.11542

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zbMATH Keywords

clustering

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semidefinite programming

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branch-and-bound

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describes a project that uses

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Evaluating a branch-and-bound RLT-based algorithm for minimum sum-of-squares clustering

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An improved column generation algorithm for minimum sum-of-squares clustering

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NP-hardness of Euclidean sum-of-squares clustering

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Q2934696

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Relax, No Need to Round

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Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems

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A repetitive branch-and-bound procedure for minimum within-cluster sums of squares partitioning

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Q5180702

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sGDML: constructing accurate and data efficient molecular force fields using machine learning

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The partition problem

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Constrained clustering by constraint programming

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SDP-Based Bounds for the Quadratic Cycle Cover Problem via Cutting-Plane Augmented Lagrangian Methods and Reinforcement Learning

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The Ratio-Cut Polytope and K-Means Clustering

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Evaluation of a Branch and Bound Algorithm for Clustering

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An Interior Point Algorithm for Minimum Sum-of-Squares Clustering

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A branch-and-cut algorithm based on semidefinite programming for the minimum $k$-partition problem

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J-MEANS: A new local search heuristic for minimum sum of squares clustering

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Probably certifiably correct $k$-means clustering

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Rigorous Error Bounds for the Optimal Value in Semidefinite Programming

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A local search approximation algorithm for $k$-means clustering

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New diagonal bundle method for clustering problems in large data sets

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A Branch and Bound Clustering Algorithm

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New and efficient DCA based algorithms for minimum sum-of-squares clustering

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When do birds of a feather flock together? $k$-means, proximity, and conic programming

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Least squares quantization in PCM

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Q5617409

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The planar $k$-means problem is NP-hard

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Clustering subgaussian mixtures by semidefinite programming

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Approximating K‐means‐type Clustering via Semidefinite Programming

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Q5701392

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Improved Conic Reformulations for $K$-means Clustering

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Cluster Analysis and Mathematical Programming

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Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

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Q4247442

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A global optimization RLT-based approach for solving the hard clustering problem

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SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints

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A Newton-CG Augmented Lagrangian Method for Semidefinite Programming

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Identifiers

Mathematics Subject Classification ID

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10.1287/IJOC.2022.1166

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Sitelinks

Mathematics(1 entry)

mardi Publication:5106412