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A Feasible-Point Sagitta Approach in Linear Programming

by Á. Santos Palomo, P. Guerrero García
"... Abstract. A second scheme for the sagitta method is presented. This method uses a “global ” viewpoint of the linear problem, and, in this feasible-point version, it also takes advantage of the additional “local” information that a feasible point supplies. The computational results obtained are highl ..."
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Abstract. A second scheme for the sagitta method is presented. This method uses a “global ” viewpoint of the linear problem, and, in this feasible-point version, it also takes advantage of the additional “local” information that a feasible point supplies. The computational results obtained

Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization

by Farid Alizadeh - 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

A Critical Point For Random Graphs With A Given Degree Sequence

by Michael Molloy, Bruce Reed , 2000
"... Given a sequence of non-negative real numbers 0 ; 1 ; : : : which sum to 1, we consider random graphs having approximately i n vertices of degree i. Essentially, we show that if P i(i \Gamma 2) i ? 0 then such graphs almost surely have a giant component, while if P i(i \Gamma 2) i ! 0 the ..."
Abstract - Cited by 511 (8 self) - Add to MetaCart
Given a sequence of non-negative real numbers 0 ; 1 ; : : : which sum to 1, we consider random graphs having approximately i n vertices of degree i. Essentially, we show that if P i(i \Gamma 2) i ? 0 then such graphs almost surely have a giant component, while if P i(i \Gamma 2) i ! 0 then almost surely all components in such graphs are small. We can apply these results to G n;p ; G n;M , and other well-known models of random graphs. There are also applications related to the chromatic number of sparse random graphs.

Lag length selection and the construction of unit root tests with good size and power

by Serena Ng, Pierre Perron - Econometrica , 2001
"... It is widely known that when there are errors with a moving-average root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We conside ..."
Abstract - Cited by 534 (14 self) - Add to MetaCart
framework in which the moving-average root is local to −1 to document how the MIC performs better in selecting appropriate values of k. In monte-carlo experiments, the MIC is found to yield huge size improvements to the DF GLS and the feasible point optimal PT test developed in Elliott, Rothenberg and Stock

On Proving Existence of Feasible Points in Equality Constrained Optimization Problems

by R. Baker Kearfott - Mathematical Programming , 1995
"... Various algorithms can compute approximate feasible points or approximate solutions to equality and bound constrained optimization problems. In exhaustive search algorithms for global optimizers and other contexts, it is of interest to construct bounds around such approximate feasible points, then t ..."
Abstract - Cited by 12 (6 self) - Add to MetaCart
Various algorithms can compute approximate feasible points or approximate solutions to equality and bound constrained optimization problems. In exhaustive search algorithms for global optimizers and other contexts, it is of interest to construct bounds around such approximate feasible points

Disconnected Operation in the Coda File System

by James J. Kistler, M. Satyanarayanan - ACM Transactions on Computer Systems , 1992
"... Disconnected operation is a mode of operation that enables a client to continue accessing critical data during temporary failures of a shared data repository. An important, though not exclusive, application of disconnected operation is in supporting portable computers. In this paper, we show that di ..."
Abstract - Cited by 1014 (36 self) - Add to MetaCart
that disconnected operation is feasible, efficient and usable by describing its design and implementation in the Coda File System. The central idea behind our work is that caching of data, now widely used for performance, can also be exploited to improve availability.

Qualitative Researching

by James Mason, Vasilis Fthenakis, Ken Zweibel, Tom Hansen, Thomas Nikolakakis , 1996
"... ltaic (PV) electricity production from an intermittent Since 1978, compressed air energy storage (CAES) compressed air can then be released on demand to the CAES plant’s turbo-generator set to generate premium value electricity. The first CAES plant was built in broadened in the ittency of wind g wi ..."
Abstract - Cited by 591 (0 self) - Add to MetaCart
with Cavallo,2 nomic feasibility as turbine (GT) oduction.3–8 The ate underground o the wind farms and shape the

Learning the Kernel Matrix with Semi-Definite Programming

by Gert R. G. Lanckriet, Nello Cristianini, Laurent El Ghaoui, Peter Bartlett, Michael I. Jordan , 2002
"... Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
Abstract - Cited by 780 (22 self) - Add to MetaCart
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information

Hierarchical Models of Object Recognition in Cortex

by Maximilian Riesenhuber, Tomaso Poggio , 1999
"... The classical model of visual processing in cortex is a hierarchy of increasingly sophisticated representations, extending in a natural way the model of simple to complex cells of Hubel and Wiesel. Somewhat surprisingly, little quantitative modeling has been done in the last 15 years to explore th ..."
Abstract - Cited by 817 (84 self) - Add to MetaCart
the biological feasibility of this class of models to explain higher level visual processing, such as object recognition. We describe a new hierarchical model that accounts well for this complex visual task, is consistent with several recent physiological experiments in inferotemporal cortex and makes testable

Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

by N. Srinivas, Kalyanmoy Deb - Evolutionary Computation , 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
Abstract - Cited by 524 (4 self) - Add to MetaCart
the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a
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