Results 1  10
of
1,278,412
A Stochastic QuasiNewton Method for LargeScale Optimization. arXiv preprint arXiv:1401.7020
, 2014
"... The question of how to incorporate curvature information in stochastic approximation methods is challenging. The direct application of classical quasiNewton updating techniques for deterministic optimization leads to noisy curvature estimates that have harmful effects on the robustness of the ite ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
The question of how to incorporate curvature information in stochastic approximation methods is challenging. The direct application of classical quasiNewton updating techniques for deterministic optimization leads to noisy curvature estimates that have harmful effects on the robustness
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
Abstract

Cited by 582 (23 self)
 Add to MetaCart
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
The LargeScale Organization of Metabolic Networks
, 2000
"... In a cell or microorganism the processes that generate mass, energy, information transfer, and cell fate specification are seamlessly integrated through a complex network of various cellular constituents and reactions. However, despite the key role these networks play in sustaining various cellular ..."
Abstract

Cited by 599 (7 self)
 Add to MetaCart
and errortolerant networks, and may represent a common blueprint for the largescale organization of interactions among all cellular constituents.
Making LargeScale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
Abstract

Cited by 620 (1 self)
 Add to MetaCart
Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large
SCRIBE: A largescale and decentralized applicationlevel multicast infrastructure
 IEEE Journal on Selected Areas in Communications (JSAC
, 2002
"... This paper presents Scribe, a scalable applicationlevel multicast infrastructure. Scribe supports large numbers of groups, with a potentially large number of members per group. Scribe is built on top of Pastry, a generic peertopeer object location and routing substrate overlayed on the Internet, ..."
Abstract

Cited by 648 (29 self)
 Add to MetaCart
This paper presents Scribe, a scalable applicationlevel multicast infrastructure. Scribe supports large numbers of groups, with a potentially large number of members per group. Scribe is built on top of Pastry, a generic peertopeer object location and routing substrate overlayed on the Internet
A stochastic QuasiNewton Method for online convex optimization
 In Proceedings of 11th International Conference on Artificial Intelligence and Statistics
, 2007
"... We develop stochastic variants of the wellknown BFGS quasiNewton optimization method, in both full and memorylimited (LBFGS) forms, for online optimization of convex functions. The resulting algorithm performs comparably to a welltuned natural gradient descent but is scalable to very highdimensi ..."
Abstract

Cited by 42 (3 self)
 Add to MetaCart
We develop stochastic variants of the wellknown BFGS quasiNewton optimization method, in both full and memorylimited (LBFGS) forms, for online optimization of convex functions. The resulting algorithm performs comparably to a welltuned natural gradient descent but is scalable to very high
GloMoSim: A Library for Parallel Simulation of Largescale Wireless Networks
 in Workshop on Parallel and Distributed Simulation
, 1998
"... A number of librarybased parallel and sequential network simulators have been designed. This paper describes a library, called GloMoSim (for Global Mobile system Simulator), for parallel simulation of wireless networks. GloMoSim has been designed to be extensible and composable: the communication p ..."
Abstract

Cited by 645 (30 self)
 Add to MetaCart
A number of librarybased parallel and sequential network simulators have been designed. This paper describes a library, called GloMoSim (for Global Mobile system Simulator), for parallel simulation of wireless networks. GloMoSim has been designed to be extensible and composable: the communication
Pastry: Scalable, distributed object location and routing for largescale peertopeer systems
, 2001
"... This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing scheme for widearea peertopeer applications. Pastry provides applicationlevel routing and object location in a potentially very large overlay network of nodes connected via the Internet. ..."
Abstract

Cited by 2063 (50 self)
 Add to MetaCart
This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing scheme for widearea peertopeer applications. Pastry provides applicationlevel routing and object location in a potentially very large overlay network of nodes connected via the Internet
Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
 SIAM Journal on Optimization
, 1993
"... We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First we review the classical cone duality as specialized to S ..."
Abstract

Cited by 557 (12 self)
 Add to MetaCart
to SDP. Next we present an interior point algorithm which converges to the optimal solution in polynomial time. The approach is a direct extension of Ye's projective method for linear programming. We also argue that most known interior point methods for linear programs can be transformed in a
QuasiNewton Methods for Markov
"... The performance of Markov chain Monte Carlo methods is often sensitive to the scaling and correlations between the random variables of interest. An important source of information about the local correlation and scale is given by the Hessian matrix of the target distribution, but this is often eithe ..."
Abstract
 Add to MetaCart
on the history of previous states are in general not valid. We address this problem by using limited memory quasiNewton methods, which depend only on a fixed window of previous samples. On several real world datasets, we show that the quasiNewton sampler is more effective than standard Hamiltonian Monte Carlo
Results 1  10
of
1,278,412