Search · four archives
Search · four archives
19 papers · ranked by Valyu relevance
He Li, Naiyu Shi
In order to address the application of genetic optimization algorithms to financial investment portfolio issues, the optimal allocation rate must be high and the risk is low. This paper uses quadratic programming algorithms and genetic algorithms as well as quadratic programming algorithms, Matlab planning solutions…
Zhaosong Lu, Yong Zhang
In this paper we consider sparse approximation problems, that is, general l0 minimization problems with the l0-"norm" of a vector being a part of constraints or objective function. In particular, we first study the first-order optimality conditions for these problems. We then propose penalty decomposition (PD) methods…
Francesco Cecere, Matteo Lapucci, Davide Pucci, Marco Sciandrone
In this paper, we consider the problem of minimizing a smooth function, given as finite sum of black-box functions, over a convex set. In order to advantageously exploit the structure of the problem, for instance when the terms of the objective functions are partially separable, noisy, costly or with first-order…
Zhengshan Dong, Geng Lin, Niandong Chen
The penalty decomposition method is an effective and versatile method for sparse optimization and has been successfully applied to solve compressed sensing, sparse logistic regression, sparse inverse covariance selection, low rank minimization, image restoration, and so on. With increase in the penalty parameters, a…
Sarah M. Weinstein, Christos Davatzikos, Jimit Doshi, Kristin A. Linn + 1 more
In neuroimaging studies, multivariate methods provide a framework for studying associations between complex patterns distributed throughout the brain and neurological, psychiatric, and behavioral phenotypes. However, mitigating the influence of nuisance variables, such as confounders, remains a critical challenge in…
Long Feng, Cun‐Hui Zhang
The Lasso is biased. Concave penalized least squares estimation (PLSE) takes advantage of signal strength to reduce this bias, leading to sharper error bounds in prediction, coefficient estimation and variable selection. For prediction and estimation, the bias of the Lasso can be also reduced by taking a smaller…
Hannes Matuschek, Reinhold Kliegl, Matthias Holschneider, Susana Martinez-Conde
'Susana Martinez-Conde'] The Smoothing Spline ANOVA (SS-ANOVA) requires a specialized construction of basis and penalty terms in order to incorporate prior knowledge about the data to be fitted. Typically, one resorts to the most general approach using tensor product splines. This implies severe constraints on the…
Matteo Lapucci, Christian Kanzow
This paper provides a theoretical and numerical investigation of a penalty decomposition scheme for the solution of optimization problems with geometric constraints. In particular, we consider some situations where parts of the constraints are nonconvex and complicated, like cardinality constraints, disjunctive…
Ivan Selesnick, İlker Bayram
This paper develops a convex approach for sparse one-dimensional deconvolution that improves upon L1-norm regularization, the standard convex approach. We propose a sparsity-inducing non-separable non-convex bivariate penalty function for this purpose. It is designed to enable the convex formulation of ill-conditioned…
Vincent Guillemot, Derek Beaton, Arnaud Gloaguen, Tommy Löfstedt + 5 more
'Brian Levine' 'Nicolas Raymond' 'Arthur Tenenhaus' 'Hervé Abdi' 'Shyamal D Peddada'] We propose a new sparsification method for the singular value decomposition-called the constrained singular value decomposition (CSVD)-that can incorporate multiple constraints such as sparsification and orthogonality for the left and…
Lei Du, Kefei Liu, Xiaohui Yao, Jingwen Yan + 5 more
'Junwei Han' 'Lei Guo' 'Andrew J. Saykin' 'Li Shen' ''] Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in…
Yong Lv, Houzhuang Zhang, Cancan Yi
As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate…
Marta Karas, Damian Brzyski, Mario Dzemidzic, Joaquin Goni + 3 more
A challenging problem arising in brain imaging research is principled incorporation of information from different imaging modalities. Frequently each modality is analyzed separately using, for instance, dimensionality reduction techniques which result in a loss of mutual information. We propose a novel regularization…
Karin Meyer
Multivariate estimates of genetic parameters are subject to substantial sampling variation, especially for smaller data sets and more than a few traits. A simple modification of standard, maximum likelihood procedures for multivariate analyses to estimate genetic covariances is described, which can improve estimates by…
Soumyajit Mandal, Preetam Mukherjee
This paper describes MMP, a three-stage framework for systematic quantum optimization of constrained molecular docking problems. The protocol addresses the “formulation bottleneck”—the critical challenge of translating constrained optimization problems into valid QUBO (Quadratic Unconstrained Binary Optimization)…
Emanuele Quattrocchi, Baptiste Py, Adeleke Maradesa, Quentin Meyer + 2 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique widely used to evaluate the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as a model-free alternative to equivalent circuits and physical models to circumvent the inherent challenges of EIS…
Chubing Zeng, Duncan Campbell Thomas, Juan Pablo Lewinger
Associated with genomic features like gene expression, methylation, and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information, and knowledge from previous studies, that can be used post-hoc to facilitate the interpretation of a…
Tian Lu, Qinxue Chen
Energy decomposition analysis (EDA) is an important method to explore the nature of interaction between fragments in a chemical system. It can decompose the interaction energy into different physical components to understand the factors that play key roles in the interaction. This work proposes an energy decomposition…
Adeleke Maradesa, Baptiste Py, Ting Hei Wan, Mohammed B. Effat + 1 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a…