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Results 1 - 10
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1,190
A survey of dual-feasible and superadditive functions
, 2008
"... Dual-feasible functions are valuable tools that can be used to compute both lower bounds for different combinatorial problems and valid inequalities for integer programs. Several families of functions have been used in the literature. Some of them were defined explicitly, and others not. One of the ..."
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Dual-feasible functions are valuable tools that can be used to compute both lower bounds for different combinatorial problems and valid inequalities for integer programs. Several families of functions have been used in the literature. Some of them were defined explicitly, and others not. One
A Truncated Primal-Infeasible Dual-Feasible Network Interior Point Method
, 1996
"... In this paper we introduce the truncated primal-infeasible dual-feasible interior point algorithm for linear programming and describe an implementation of this algorithm for solving the minimum cost network flow problem. In each iteration, the linear system that determines the search direction is co ..."
Abstract
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Cited by 31 (3 self)
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In this paper we introduce the truncated primal-infeasible dual-feasible interior point algorithm for linear programming and describe an implementation of this algorithm for solving the minimum cost network flow problem. In each iteration, the linear system that determines the search direction
1 Using Dual Feasible Functions to Construct Fast Lower Bounds for Routing and Location Problems
"... In cutting and packing problems, Dual Feasible Functions (DFFs) represent a well established tool for deriving high quality lower bounds in very short times. A well-known DFF lower bounding approach consists of using DFFs to rapidly generate feasible values for the dual variables of a Column Generat ..."
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In cutting and packing problems, Dual Feasible Functions (DFFs) represent a well established tool for deriving high quality lower bounds in very short times. A well-known DFF lower bounding approach consists of using DFFs to rapidly generate feasible values for the dual variables of a Column
Tits, A Simple primal-dual feasible interior-point method for nonlinear programming with monotone descent
- Computational Optimization and Applications
, 2003
"... We propose and analyze a primal-dual interior point method of the “feasible ” type, with the additional property that the objective function decreases at each iteration. A distinctive feature of the method is the use of different barrier parameter values for each constraint, with the purpose of bett ..."
Abstract
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Cited by 12 (2 self)
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We propose and analyze a primal-dual interior point method of the “feasible ” type, with the additional property that the objective function decreases at each iteration. A distinctive feature of the method is the use of different barrier parameter values for each constraint, with the purpose
On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming
, 2004
"... We present a primal-dual interior-point algorithm with a filter line-search method for non-linear programming. Local and global convergence properties of this method were analyzed in previous work. Here we provide a comprehensive description of the algorithm, including the feasibility restoration ph ..."
Abstract
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Cited by 294 (6 self)
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We present a primal-dual interior-point algorithm with a filter line-search method for non-linear programming. Local and global convergence properties of this method were analyzed in previous work. Here we provide a comprehensive description of the algorithm, including the feasibility restoration
Primal-Dual Interior-Point Methods for Self-Scaled Cones
- SIAM Journal on Optimization
, 1995
"... In this paper we continue the development of a theoretical foundation for efficient primal-dual interior-point algorithms for convex programming problems expressed in conic form, when the cone and its associated barrier are self-scaled (see [9]). The class of problems under consideration includes li ..."
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Cited by 206 (12 self)
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In this paper we continue the development of a theoretical foundation for efficient primal-dual interior-point algorithms for convex programming problems expressed in conic form, when the cone and its associated barrier are self-scaled (see [9]). The class of problems under consideration includes
Primal-Dual Path-Following Algorithms for Semidefinite Programming
- SIAM Journal on Optimization
, 1996
"... This paper deals with a class of primal-dual interior-point algorithms for semidefinite programming (SDP) which was recently introduced by Kojima, Shindoh and Hara [11]. These authors proposed a family of primal-dual search directions that generalizes the one used in algorithms for linear programmin ..."
Abstract
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Cited by 165 (12 self)
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This paper deals with a class of primal-dual interior-point algorithms for semidefinite programming (SDP) which was recently introduced by Kojima, Shindoh and Hara [11]. These authors proposed a family of primal-dual search directions that generalizes the one used in algorithms for linear
Characterizing the capacity region in multi-radio multi-channel wireless mesh networks
- in ACM MobiCom
, 2005
"... Next generation fixed wireless broadband networks are being increasingly deployed as mesh networks in order to provide and extend access to the internet. These networks are characterized by the use of multiple orthogonal channels and nodes with the ability to simultaneously communicate with many nei ..."
Abstract
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Cited by 244 (0 self)
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network model that captures the key practical aspects of such systems and characterize the constraints binding their behavior. We provide necessary conditions to verify the feasibility of rate vectors in these networks, and use them to derive upper bounds on the capacity in terms of achievable throughput
A NUMERICALLY STABLE DUAL METHOD FOR SOLVING STRICTLY CONVEX QUADRATIC PROGRAMS
, 1983
"... An efficient and numerically stable dual algorithm for positive definite quadratic programming is described which takes advantage of the fact lhat the unconstrained minimum of the objective function can be used as a starling point. Its implementation utilizes the Cholesky and QR factorizations and p ..."
Abstract
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Cited by 164 (0 self)
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principal motivation for the development of the algorithm). These computational results indicate that the dual algorithm is superior to primal algorithms when a primal feasible point is not readily available. The algorithm is also compared theoretically to the modified-simplex type dual methods of Lemke
Primal-dual subgradient methods for convex problems
, 2005
"... (after revision) In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure. Our methods are primaldual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual ..."
Abstract
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Cited by 143 (3 self)
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for aggregating the support functions in the dual space, and the second one establishes a dynamically updated scale between the primal and dual spaces. This additional flexibility allows to guarantee a boundedness of the sequence of primal test points even in the case of unbounded feasible set. We present
Results 1 - 10
of
1,190
