Copertina

Mixed-integer linear programming for computing optimal experimental designs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Harman, Radoslav, Rosa, Samuel
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Computation 62K05, 62J05, 90C11, 90C22
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Removal of Redundant Candidate Points for the Exact D-Optimal Design Problem
di: Harman, Radoslav, et al.
Pubblicazione: (2025)

A Randomized Exchange Algorithm for Optimal Design of Multi-Response Experiments
di: Somogyi, Pál, et al.
Pubblicazione: (2024)

An optimal experimental design approach to sensor placement in continuous stochastic filtering
di: Pathiraja, Sahani, et al.
Pubblicazione: (2025)

Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data
di: Harris, Elizabeth, et al.
Pubblicazione: (2024)

Gradient flow for finding E-optimal designs
di: Shi, Jieling, et al.
Pubblicazione: (2026)

Global optimality conditions for sensor placement, with extensions to binary low-rank A-optimal designs
di: Aarset, Christian
Pubblicazione: (2024)

Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements
di: Wang, Jialu, et al.
Pubblicazione: (2024)

Optimal designs for discrete choice models via graph Laplacians
di: Röttger, Frank, et al.
Pubblicazione: (2022)

Mean--Variance Risk-Aware Bayesian Optimal Experimental Design for Nonlinear Models
di: Shen, Wanggang, et al.
Pubblicazione: (2026)

Mixed-integer linearity in nonlinear optimization: a trust region approach
di: De Marchi, Alberto
Pubblicazione: (2023)

Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
di: Shen, Wanggang, et al.
Pubblicazione: (2021)

Stochastic dynamic programming with non-linear discounting
di: Bäuerle, Nicole, et al.
Pubblicazione: (2020)

FEM-based A-optimal sensor placement for heat source inversion from final time measurement
di: Aarset, Christian, et al.
Pubblicazione: (2025)

Determining a credit transition matrix from cumulative default probabilities
di: Gzyl, Henryk, et al.
Pubblicazione: (2025)

The Polytope of Optimal Approximate Designs: Extending the Selection of Informative Experiments
di: Harman, Radoslav, et al.
Pubblicazione: (2023)

Variational Sequential Optimal Experimental Design using Reinforcement Learning
di: Shen, Wanggang, et al.
Pubblicazione: (2023)

Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem
di: Chen, Zhongzhu, et al.
Pubblicazione: (2023)

On a wider class of prior distributions for graphical models
di: Natarajan, Abhinav, et al.
Pubblicazione: (2022)

Hybrid optimal control with mixed-integer Lagrangian methods
di: Nikitina, Viktoriya, et al.
Pubblicazione: (2024)

Affordable mixed-integer Lagrangian methods: optimality conditions and convergence analysis
di: De Marchi, Alberto
Pubblicazione: (2024)

Revisiting Madigan and Mosurski: Collapsibility via Minimal Separators
di: Heng, Pei, et al.
Pubblicazione: (2025)

Bayesian Multi-Topology Express Transportation Network Design under Posterior Predictive Demand, Sorting-Efficiency and Delivery-Time Uncertainty
di: Chatterjee, Debashis
Pubblicazione: (2026)

BISTRO -- A Bi-Fidelity Stochastic Gradient Framework using Trust-Regions for Optimization Under Uncertainty
di: Dixon, Thomas O., et al.
Pubblicazione: (2025)

Information-geometry-driven graph sequential growth
di: Bond, Harry T., et al.
Pubblicazione: (2026)

Extending graph total colorings to cell complexes
di: Dejter, Italo J.
Pubblicazione: (2026)

Multi-Iteration Stochastic Optimizers
di: Carlon, Andre, et al.
Pubblicazione: (2020)

On the power of linear programming for K-means clustering
di: De Rosa, Antonio, et al.
Pubblicazione: (2024)

Optimal Dorfman Group Testing for Symmetric Distributions
di: Landolfi, Nicholas C., et al.
Pubblicazione: (2023)

Maintenance optimization of a two-component system with mixed observability
di: Zhang, Nan, et al.
Pubblicazione: (2026)

Bayesian Linear Programming under Learned Uncertainty: Posterior Feasibility Guarantees, Scenario Certification, and Applications
di: Chatterjee, Debashis
Pubblicazione: (2026)

Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease
di: Pan, Haonan, et al.
Pubblicazione: (2025)

Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on finite spaces: A statistical perspective
di: Hundrieser, Shayan, et al.
Pubblicazione: (2022)

Continuity of Filters for Discrete-Time Control Problems Defined by Explicit Equations
di: Feinberg, Eugene A., et al.
Pubblicazione: (2023)

New finite relaxation hierarchies for concavo-convex, disjoint bilinear programs, and facial disjunctions
di: Tawarmalani, Mohit
Pubblicazione: (2024)

Almost Orthogonal Arrays: Search Three Ways
di: Martínez, Luis, et al.
Pubblicazione: (2024)

The Augmented Mixing Method: Computing High-Accuracy Primal-Dual Solutions to Large-Scale SDPs via Column Updates
di: Brosch, Daniel, et al.
Pubblicazione: (2025)

Iterative Exploration-Driven Sparse SDP Clustering via Thompson Sampling
di: Mun, Jongmin, et al.
Pubblicazione: (2025)

A structured L-BFGS method and its application to inverse problems
di: Mannel, Florian, et al.
Pubblicazione: (2023)

A structured L-BFGS method with diagonal scaling and its application to image registration
di: Mannel, Florian, et al.
Pubblicazione: (2024)

On Difference-of-SOS and Difference-of-Convex-SOS Decompositions for Polynomials
di: Niu, Yi-Shuai, et al.
Pubblicazione: (2018)