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A General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
2011/7/7
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimi...
An Integrated Forward/Reverse Logistics Network Optimization Model for Multi-Stage Capacitated Supply Chain
Integrated Forward/Reverse Logistics Network Closed-Loop Supply Chain Network Mixed-Integer Linear Programming Multi-Objective Optimization Capacity and Location Decision
2013/2/23
In this study, the integrated forward/reverse logistics network is investigated, and a capacitated multi-stage logistics network design is proposed by formulating a generalized logistics network probl...
All-at-once Optimization for Coupled Matrix and Tensor Factorizations
data fusion matrix factorizations tensor factorizations CANDECOMP PARAFAC missing data
2011/6/21
Joint analysis of data from multiple sources has the potential
to improve our understanding of the underlying structures
in complex data sets. For instance, in restaurant recommendation
systems, re...
Generalized Boosting Algorithms for Convex Optimization
Generalized Boosting Algorithms Convex Optimization
2011/6/21
Boosting is a popular way to derive power-
ful learners from simpler hypothesis classes.
Following previous work (Mason et al., 1999;
Friedman, 2000) on general boosting frame-
works, we analyze g...
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns ...
Exact block-wise optimization in group lasso for linear regression
Block coordinate descent convex optimization group LASSO sparse group LASSO
2010/10/19
The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the ...
Bid Optimization for Internet Graphical Ad Auction Systems via Special Ordered Sets
Optimization Advertising Electronic Business
2013/2/23
This paper describes an optimization model for setting bid levels for certain types of advertisements on web pages. This model is non-convex, but we are able to obtain optimal or near-optimal solution...
Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth
Multivariate quantile quantile regression halfspace depth
2010/3/10
A new multivariate concept of quantile, based on a directional
version of Koenker and Bassett’s traditional regression quantiles, is
introduced for multivariate location and multiple-output regressi...
Discussion of “Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth”
Multivariate quantiles multiple-output regression quantiles L1 optimization halfspace depth
2010/3/10
First I would like to congratulate the authors for developing a new concept
of directional quantile contours. The work will contribute well to the pursuit
of multivariate quantiles. The multiple out...
Feature Extraction for Universal Hypothesis Testing via Rank-constrained Optimization
Universal test mismatched universal test hypothesistesting feature extraction exponential family
2010/3/9
This paper concerns the construction of universal
tests for binary hypothesis testing, in which the alternate hypothesis
is poorly modeled and the observation space is large.
The mismatched univers...
Snell's optimization problem for sequences of convex compact valued random sets
Snell's optimization problem sequences of convex valued random sets
2009/9/21
A random set analogue of the Snell problem is presented.
In the original Snell's problem one observes a sequence of random
variables (t,), say a gambler's capital at successive games. If the gambler...
Portfolio Optimization with Non-Constant Volatility and Partial Information
Portfolio Optimization Non-Constant Volatility Partial Information
2009/9/17
Portfolio Optimization with Non-Constant Volatility and Partial Information。
Optimization of touristic distribution networks using genetic algorithms
Distribution networks vehicle routing problem tourism demand air transportation genetic algorithms edge mapped recombination operator
2009/2/23
The eight basic elements to design genetic algorithms (GA) are described and applied to
solve a low demand distribution problem of passengers for a hub airport in Alicante and 30
touristic destinati...
Production Scheduling Optimization in the Casting Industry using Genetic Algorithms
production scheduling casting industry optimization
2018/3/12
This paper presents the use and results of production scheduling in the casting industry
with the application of the Genetic Algorithms technique. In the casting industry, there
are two important an...
Modeling and Results of Satellite Ground Support Optimization
LEO spacecraft Ground station support Product mix Allocation
2018/3/9
This paper will present the methods adapted to model low earth orbiting (LEO)
spacecraft support and an actual implementation of this approach. The results of a
comparison of actual human schedulers...