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Found 13 papers, 7 papers with code
Block Majorization Minimization with Extrapolation and Application to $β$-NMF
We propose a Block Majorization Minimization method with Extrapolation (BMMe) for solving a class of multi-convex optimization problems.
Deep Nonnegative Matrix Factorization with Beta Divergences
Deep Nonnegative Matrix Factorization (deep NMF) has recently emerged as a valuable technique for extracting multiple layers of features across different scales.
Anomaly detection with semi-supervised classification based on risk estimators
A significant limitation of one-class classification anomaly detection methods is their reliance on the assumption that unlabeled training data only contains normal instances.
Multiblock ADMM for nonsmooth nonconvex optimization with nonlinear coupling constraints
This paper proposes a multiblock alternating direction method of multipliers for solving a class of multiblock nonsmooth nonconvex optimization problem with nonlinear coupling constraints.
Block Alternating Bregman Majorization Minimization with Extrapolation
In this paper, we consider a class of nonsmooth nonconvex optimization problems whose objective is the sum of a block relative smooth function and a proper and lower semicontinuous block separable function.
A Framework of Inertial Alternating Direction Method of Multipliers for Non-Convex Non-Smooth Optimization
In this paper, we propose an algorithmic framework, dubbed inertial alternating direction methods of multipliers (iADMM), for solving a class of nonconvex nonsmooth multiblock composite optimization problems with linear constraints.
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization
In this paper, we introduce TITAN, a novel inerTIal block majorizaTion minimizAtioN framework for non-smooth non-convex optimization problems.
Algorithms for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence
Nonnegative matrix factorization (NMF) is a standard linear dimensionality reduction technique for nonnegative data sets.
Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization
This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF).
Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization
We introduce and analyze BPALM and A-BPALM, two multi-block proximal alternating linearized minimization algorithms using Bregman distances for solving structured nonconvex problems.
Optimization and Control Numerical Analysis Numerical Analysis
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
We propose inertial versions of block coordinate descent methods for solving non-convex non-smooth composite optimization problems.
Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization
We propose to use Lagrange duality to judiciously optimize for a set of weights to be used within the framework of the weighted-sum approach, that is, we minimize a single objective function which is a weighted sum of the all objective functions.
Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization
Avoiding this devise, we propose an accelerated randomized mirror descent method for solving this problem without the strongly convex assumption.
