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149
SEMIDEFINITE PROGRAMMING RELAXATIONS FOR THE GRAPH PARTITIONING PROBLEM
, 1999
"... A new semidefinite programming, SDP, relaxation for the general graph partitioning problem, GP, is derived. The relaxation arises from the dual of the (homogenized) Lagrangian dual of an appropriate quadratic representation of GP. The quadratic representation includes a representation of the 0,1 co ..."
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Cited by 31 (6 self)
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feasibility is enforced, which results in the desired lower bounds, but avoids the expensive primal feasibility calculations. Numerical results
A new warmstarting strategy for the primaldual column generation method.
 Mathematical Programming
, 2014
"... Abstract This paper presents a new warmstarting technique in the context of a primaldual column generation method applied to solve a particular class of combinatorial optimization problems. The technique relies on calculating an initial point and on solving auxiliary linear optimization problems t ..."
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Cited by 3 (1 self)
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Abstract This paper presents a new warmstarting technique in the context of a primaldual column generation method applied to solve a particular class of combinatorial optimization problems. The technique relies on calculating an initial point and on solving auxiliary linear optimization problems
Primaldual relationship between LevenbergMarquardt and central trajectories for linearly constrained convex optimization
"... Abstract We consider the minimization of a convex function on a compact polyhedron defined by linear equality constraints and nonnegative variables. We define the LevenbergMarquardt (LM) and central trajectories starting at the analytic center and using the same parameter, and show that they sati ..."
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that they satisfy a primaldual relationship, being close to each other for large values of the parameter. Based on this we develop an algorithm that starts computing primaldual feasible points on the LM trajectory and eventually moves to the central path. Our main theorem is particularly relevant in quadratic
Global Convergence of a Class of Trust Region Algorithms for Optimization Using Inexact Projections on Convex Constraints
, 1995
"... A class of trust region based algorithms is presented for the solution of nonlinear optimization problems with a convex feasible set. At variance with previously published analysis of this type, the theory presented allows for the use of general norms. Furthermore, the proposed algorithms do not r ..."
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Cited by 72 (6 self)
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not require the explicit computation of the projected gradient, and can therefore be adapted to cases where the projection onto the feasible domain may be expensive to calculate. Strong global convergence results are derived for the class. It is also shown that the set of linear and nonlinear constraints
A FEASIBILITY STUDY OF CONTRACT FINISHING OF HOGS
"... A multi year financial model was used to evaluate the economics of contract finishing of hogs. The model includes projected income statements, balance sheets, and cash flow statements, as well as the calculation of the Internal Rate of Return (IRR) using discounted after tax cash flows. The model us ..."
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, and the cost of spreading the manure as expenses. The effect of an injection of patient capital was also calculated. The calculations were done for a 20 year period, from the time the facility is built and stocked with hogs to the end of the serviceable life of the barn. The results indicate
Parallel selfconsistentfield calculations using chebyshevfiltered subspace acceleration
 Physical Review E
, 2006
"... Solving the KohnSham eigenvalue problem constitutes the most computationally expensive part in selfconsistent density functional theory (DFT) calculations. A nonlinear Chebyshevfiltered subspace iteration is developed which avoids computing explicit eigenvectors, except at the first SCF iteration ..."
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Cited by 21 (5 self)
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Solving the KohnSham eigenvalue problem constitutes the most computationally expensive part in selfconsistent density functional theory (DFT) calculations. A nonlinear Chebyshevfiltered subspace iteration is developed which avoids computing explicit eigenvectors, except at the first SCF
Feasible CrossValidatory Model Selection For General Stationary Processes
 Journal of Applied Econometrics
, 1997
"... . Crossvalidation is a method used to estimate the expected prediction error of a model. Such estimates may be of interest in themselves, but their use for model selection is more common. Unfortunately, crossvalidation is viewed as being computationally expensive in many situations. In this pape ..."
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Cited by 9 (3 self)
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. Crossvalidation is a method used to estimate the expected prediction error of a model. Such estimates may be of interest in themselves, but their use for model selection is more common. Unfortunately, crossvalidation is viewed as being computationally expensive in many situations
Title: Feasibility Study of Centralized Distribution in a Regional Distributor of Building Materials
"... This paper tests the feasibility of a regional building products supplier converting to a centralized distribution system from the current decentralized system which expensively provides unsatisfactory customer service. This research was conducted in an effort to improve the company's logistics ..."
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This paper tests the feasibility of a regional building products supplier converting to a centralized distribution system from the current decentralized system which expensively provides unsatisfactory customer service. This research was conducted in an effort to improve the company
AN ANALYSIS OF THE FEASIBILITY AND ENVIRONMENTAL IMPACT OF INCORPORATING CLEAN ENERGY INTO AN ISLANDED MICROGRID IN
, 2014
"... Clarity Project is a fine jewelry company and social enterprise that recently began mining diamonds in Sierra Leone as part of its mission to improve the quality of life of artisanal miners in West African communities. This has presented a new challenge to the company: the site of Clarity Project’s ..."
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the use of diesel generators and battery storage to supplement the solar power. The model then calculates the present value of the capital and operating expenses for the microgrid as well as the carbon dioxide emissions associated with generating electricity for the compound. Our analysis has determined
1 Estimating Feasibility Using Multiple Surrogates and ROC Curves
"... Constraint optimization aims at finding optimum points that satisfy equality or inequality constraints. An important part of constraint optimization is to estimate the feasibility of a point to be added in the next optimization cycle. This is especially evident in realworld problems which have mult ..."
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design of experiment (DOE) when constraint calculations are computationally expensive, making the use of surrogates imperative. The method does not require additional resources and it is not limited to any particular choice of surrogate. Three different ways of predicting feasibility are described, where
Results 1  10
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149