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2017第十届“大规模系统开发管理”国际会议（2017 Tenth International Conference "Management of Large-Scale System Development" (MLSD)）
2017 第十届 大规模系统开发管理 国际会议
2017/9/14
International Conference «Management of large-scale system development»(MLSD) is cross-disciplinary forum of academicians, researchers, teachers and practicians. Since 2007 Conferen...
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
logistic regression feature selection regularization regularization path
2015/7/10
Logistic regression with l1 regularization has been proposed as a promising method for feature selection in classification problems. In this paper we describe an efficient interior-point method for so...
We consider the problem of choosing the gate sizes or scale factors in a combinational logic circuit in order to minimize the total area, subject to simple RC timing constraints, and a minimum allowed...
Block Splitting for Large-Scale Distributed Learning
Block Splitting Large-Scale Distributed Learning
2015/7/9
Machine learning and statistics with very large datasets is now a topic of widespread interest, both in academia and industry. Many such tasks can be posed as convex optimization problems, so algorith...
Network Lasso: Clustering and Optimization in Large Graphs
Convex Optimization ADMM Network Lasso
2015/7/8
Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimiza...
Numerical Computation of Large Deviations Exponents via Simulation
Large deviations simulation regeneration eigenvalue problem,distribution arkov chains
2015/7/8
Consider the problem of numerically computing the exponential rate at which the tail of the hitting time of a Markov chain to a given set decreases to zero. One approach involves computing the solutio...
Laws of Large Numbers and Functional Central Limit Theorems for Generalized Semi-Markov Processes
Central limit theorem Discrete-event stochastic systems Generalized semi-Markov processes
2015/7/6
Because of the fundamental role played by generalized semi-Markov processes (GSMPs) in the modeling and analysis of complex discrete-event stochastic systems, it is important to understand the conditi...
A Large Deviations View of Asymptotic Efficiency for Simulation Estimators
Large Deviations View Asymptotic Efficiency Simulation Estimators
2015/7/6
Consider a simulation estimator α(c) based on expending c units of computer time, to estimate a quantity α. One measure of efficiency is to attempt to minimize P(|α(c)−α|>ε) for large c. This he...
Large Deviations for the Empirical Mean of an M/M/1 Queue
Large Deviations Empirical Mean M/M/1 Queue
2015/7/6
The theory of large deviations for random walks is important both in its own right and as a starting point for establishing large deviations for more complex models (such as ìsmall noiseîdi§usion...
Central Limit Theorems and Large Deviations for Additive Functionals of Reflecting Diffusion Processes
Central Limit Theorems Large Deviations Additive Functionals Reflecting Diffusion Processes
2015/7/6
This paper develops central limit theorems (CLT's) and large deviations results for additive functionals associated with reflecting disions in which the functional may include a term associated with t...
User’s Guide for SNOPT Version 7: Software for Large-Scale Nonlinear Programming
optimization large-scale nonlinear programming nonlinear constraints
2015/7/3
SNOPT is a general-purpose system for constrained optimization. It minimizes a
linear or nonlinear function subject to bounds on the variables and sparse linear or
nonlinear constraints. It is suita...
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
large-scale optimization nonlinear programming
2015/7/3
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective
and constraints.Here w...
Stable and efficient updates to the basis matrix factors are vital to the simplex
method. The "best" updating method depends on the machine in use and how the update is implemented. For example, the ...
LARGE-SCALE LINEARLY CONSTRAINED OPTIMIZATION
Large-scale Systems Linear Constraints Linear Programming
2015/7/3
An algorithm for solving large-scale nonlinear' programs with linear constraints is presented.
The method combines efficient sparse-matrix techniques as in the revised simplex method
with stable q...
An Innovative Approach about the Process Knowledge Representation in the Processes of Large Cluster Projects Management
Cluster Projects Process Knowledge Knowledge Representation
2013/2/23
The features of the large Cluster projects’ management processes, complexity, “one-off” and “irreversibility”, make the projects’ process knowledge hard to be expressed, analyzed and obtained accurate...