Imagen de Portada

P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kronqvist, Jan, Misener, Ruth, Tsay, Calvin
Formato:	Preprint
Publicado:	2022
Materias:	Optimization and Control Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Global optimization of graph acquisition functions for neural architecture search
por: Xie, Yilin, et al.
Publicado: (2025)

Relaxation-Informed Training of Neural Network Surrogate Models
por: Tsay, Calvin
Publicado: (2026)

On disjunction convex hulls by lifting
por: Qu, Yushan, et al.
Publicado: (2024)

ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
por: Liu, Yu, et al.
Publicado: (2025)

Training Neural ODEs Using Fully Discretized Simultaneous Optimization
por: Shapovalova, Mariia, et al.
Publicado: (2025)

A constrained optimization approach to improve robustness of neural networks
por: Zhao, Shudian, et al.
Publicado: (2024)

Bayesian optimization as a flexible and efficient design framework for sustainable process systems
por: Paulson, Joel A., et al.
Publicado: (2024)

BoGrape: Bayesian optimization over graphs with shortest-path encoded
por: Xie, Yilin, et al.
Publicado: (2025)

Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
por: Langdon, Becky, et al.
Publicado: (2026)

When Deep Learning Meets Polyhedral Theory: A Survey
por: Huchette, Joey, et al.
Publicado: (2023)

Verifying message-passing neural networks via topology-based bounds tightening
por: Hojny, Christopher, et al.
Publicado: (2024)

Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
por: Xie, Yilin, et al.
Publicado: (2024)

An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
por: Tang, Wei-Ting, et al.
Publicado: (2026)

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
por: Cheon, Mujin, et al.
Publicado: (2024)

A robust and adaptive MPC formulation for Gaussian process models
por: Dubied, Mathieu, et al.
Publicado: (2025)

BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
por: Boyne, Toby, et al.
Publicado: (2025)

A parameterized linear formulation of the integer hull
por: Eisenbrand, Friedrich, et al.
Publicado: (2025)

A learning-based mathematical programming formulation for the automatic configuration of optimization solvers
por: Iommazzo, Gabriele, et al.
Publicado: (2024)

Binary Kernel Logistic Regression: a sparsity-inducing formulation and a convergent decomposition training algorithm
por: Consolo, Antonio, et al.
Publicado: (2025)

Learning linear dynamical systems under convex constraints
por: Tyagi, Hemant, et al.
Publicado: (2023)

Scaling Mixed-Integer Programming for Certification of Neural Network Controllers Using Bounds Tightening
por: Sosnin, Philip, et al.
Publicado: (2024)

Beyond adjacency: Graph encoding with reachability and shortest paths
por: Zhang, Shiqiang, et al.
Publicado: (2025)

Risk reversal for least squares estimators under nested convex constraints
por: Al-Ghattas, Omar
Publicado: (2026)

Randomized multi-class classification under system constraints: a unified approach via post-processing
por: Chzhen, Evgenii, et al.
Publicado: (2025)

First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
por: Lu, Zhaosong, et al.
Publicado: (2025)

Online Non-convex Optimization with Long-term Non-convex Constraints
por: Pan, Shijie, et al.
Publicado: (2023)

Extended formulations for the integer hull of strictly $Δ$-modular cographic polyhedral cones
por: Paat, Joseph, et al.
Publicado: (2023)

Schrödinger bridge for generative AI: Soft-constrained formulation and convergence analysis
por: Ma, Jin, et al.
Publicado: (2025)

Tightening big Ms in integer programming formulations for support vector machines with ramp loss
por: Baldomero-Naranjo, Marta, et al.
Publicado: (2024)

A Quantile Neural Network Framework for Two-stage Stochastic Optimization
por: Alcántara, Antonio, et al.
Publicado: (2024)

Sharpness and well-conditioning of nonsmooth convex formulations in statistical signal recovery
por: Ding, Lijun, et al.
Publicado: (2023)

Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion
por: Cutkosky, Ashok, et al.
Publicado: (2023)

Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations
por: Carrasco, Pablo, et al.
Publicado: (2024)

Tightening the mixed integer linear formulation for the piecewise linear approximation in general dimensions
por: Ploussard, Quentin, et al.
Publicado: (2025)

On the convex hull of the graph of a simple monomial
por: Lee, Jon, et al.
Publicado: (2026)

On semidefinite descriptions for convex hulls of quadratic programs
por: Wang, Alex L., et al.
Publicado: (2024)

Nesterov acceleration in benignly non-convex landscapes
por: Gupta, Kanan, et al.
Publicado: (2024)

Non-geodesically-convex optimization in the Wasserstein space
por: Luu, Hoang Phuc Hau, et al.
Publicado: (2024)

A simple and improved algorithm for noisy, convex, zeroth-order optimisation
por: Carpentier, Alexandra
Publicado: (2024)

Decentralized Non-convex Stochastic Optimization with Heterogeneous Variance
por: Chen, Hongxu, et al.
Publicado: (2026)