On the practically interesting instances of MAXCUT [PDF]
, 2012 The complexity of a computational problem is traditionally quantified based on the hardness of its worst case. This approach has many advantages and has led to a deep and beautiful theory.
Bilu, Yonatan +3 more
core +6 more sources
Tropicalizing the simplex algorithm [PDF]
, 2013 We develop a tropical analog of the simplex algorithm for linear programming. In particular, we obtain a combinatorial algorithm to perform one tropical pivoting step, including the computation of reduced costs, in O(n(m+n)) time, where m is the number ...
Allamigeon, Xavier +3 more
core +5 more sources
Combining Column Generation and Lagrangian Relaxation [PDF]
, 2003 Although the possibility to combine column generation and Lagrangian relaxation has been known for quite some time, it has only recently been exploited in algorithms. In this paper, we discuss ways of combining these techniques.
Huisman, D. (Dennis) +3 more
core +7 more sources
An interior point algorithm for minimum sum-of-squares clustering [PDF]
, 1999 Copyright @ 2000 SIAM PublicationsAn exact algorithm is proposed for minimum sum-of-squares nonhierarchical clustering, i.e., for partitioning a given set of points from a Euclidean m-space into a given number of clusters in order to minimize the sum of ...
Du Merle, O +3 more
core +2 more sources
Efficient algorithm for solving semi-infinite programming problems and their applications to nonuniform filter bank designs [PDF]
, 2006 An efficient algorithm for solving semi-infinite programming problems is proposed in this paper. The index set is constructed by adding only one of the most violated points in a refined set of grid points.
Ho, C, Ling, B, Liu, Y, Tam, P, Teo, K
core +1 more source
Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms [PDF]
, 2014 Kuznetsov independence of variables X and Y means that, for any pair of bounded functions f(X)f(X) and g(Y)g(Y), E[f(X)g(Y)]=E[f(X)]⊠E[g(Y)]E[f(X)g(Y)]=E[f(X)]⊠E[g(Y)], where E[⋅]E[⋅] denotes interval-valued expectation and ⊠ denotes interval ...
Cozman, Fabio G., de Campos, Cassio P.
core +1 more source
Unsupervised Multi Class Segmentation of 3D Images with Intensity Inhomogeneities
, 2017 Intensity inhomogeneities in images constitute a considerable challenge in image segmentation. In this paper we propose a novel biconvex variational model to tackle this task.
Fitschen, Jan Henrik +2 more
core +1 more source
SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics
Advanced Science, EarlyView.ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao +11 more
wiley +1 more source
Authoring Terrestrial Planets with Diffusion Models
Computer Graphics Forum, EarlyView.Abstract To support the design and subsequent generation of terrestrial planets for use in the creative media, we propose a solution that employs a generative model trained on satellite data from planetary bodies with a defined solid surface, such as the Earth and Mars.
Oliver Borg +6 more
wiley +1 more source
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes [PDF]
, 2010 Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions reliably.
Parr, Ron +3 more
core +2 more sources