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Results 1 - 10
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1,544
A Homogeneous Interior-point Algorithm for . . .
"... A homogeneous infeasible-start interior-point algorithm for solving nonsymmetric convex conic optimization problems is presented. Starting each iteration from the vicinity of the central path, the method steps in the approximate tangent direction and then applies a correction phase to locate the ne ..."
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A homogeneous infeasible-start interior-point algorithm for solving nonsymmetric convex conic optimization problems is presented. Starting each iteration from the vicinity of the central path, the method steps in the approximate tangent direction and then applies a correction phase to locate
Interior Point Algorithms for Integer Programming
, 1994
"... Research on using interior point algorithms to solve integer programming problems is surveyed. This paper concentrates on branch and bound and cutting plane methods; a potential function method is also briefly mentioned. The principal difficulty with using an interior point algorithm in a branch and ..."
Abstract
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Cited by 6 (4 self)
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Research on using interior point algorithms to solve integer programming problems is surveyed. This paper concentrates on branch and bound and cutting plane methods; a potential function method is also briefly mentioned. The principal difficulty with using an interior point algorithm in a branch
An Interior-Point Algorithm For Nonconvex Nonlinear Programming
- COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
, 1997
"... The paper describes an interior-point algorithm for nonconvex nonlinear programming which is a direct extension of interior--point methods for linear and quadratic programming. Major modifications include a merit function and an altered search direction to ensure that a descent direction for the mer ..."
Abstract
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Cited by 199 (14 self)
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The paper describes an interior-point algorithm for nonconvex nonlinear programming which is a direct extension of interior--point methods for linear and quadratic programming. Major modifications include a merit function and an altered search direction to ensure that a descent direction
Interior-point algorithms for linear-programming decoding
, 2008
"... Interior-point algorithms constitute a very interesting class of algorithms for solving linear-programming problems. In this paper we study efficient implementations of such algorithms for solving the linear program that appears in the linear-programming decoder formulation. ..."
Abstract
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Cited by 9 (0 self)
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Interior-point algorithms constitute a very interesting class of algorithms for solving linear-programming problems. In this paper we study efficient implementations of such algorithms for solving the linear program that appears in the linear-programming decoder formulation.
Interior Point Algorithms
, 1997
"... Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today. It is adaptable, easy to interpret, and produces highly accurate mod-els. However, most implementations today are computationally ex-pensive and require all training data t ..."
Abstract
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Cited by 16 (0 self)
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Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today. It is adaptable, easy to interpret, and produces highly accurate mod-els. However, most implementations today are computationally ex-pensive and require all training data
On Some Interior-Point Algorithms for Nonconvex Quadratic Optimization
- Math. Program
, 2000
"... Recently, interior-point algorithms have been applied to nonlinear and nonconvex optimization. Most of these algorithms are either primal-dual path-following or anescaling in nature, and some of them are conjectured to converge to a local minimum. We give several examples to show that this may be un ..."
Abstract
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Cited by 3 (1 self)
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Recently, interior-point algorithms have been applied to nonlinear and nonconvex optimization. Most of these algorithms are either primal-dual path-following or anescaling in nature, and some of them are conjectured to converge to a local minimum. We give several examples to show that this may
Smoothed Analysis of Interior-Point Algorithms: Termination
, 2003
"... We perform a smoothed analysis of the termination phase of an interior-point method. By combining this analysis with the smoothed analysis of Renegar’s interior-point algorithm in [DST02], we show that the smoothed complexity of an interior-point algorithm for linear programming is O(m 3 log(m/σ)). ..."
Abstract
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Cited by 3 (1 self)
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We perform a smoothed analysis of the termination phase of an interior-point method. By combining this analysis with the smoothed analysis of Renegar’s interior-point algorithm in [DST02], we show that the smoothed complexity of an interior-point algorithm for linear programming is O(m 3 log
PRIMAL-DUAL INTERIOR-POINT ALGORITHMS:
"... INTRODUCTION Spring 1995 We consider linear programming problems in the following primal (P ) and dual (D) forms: (P ) maximize c T x Ax = b; x 0; (D) minimize b T y A T y \Gamma s = c; s 0; where A 2 IR m\Thetan , b 2 IR m , and c 2 IR n (all vectors are column vectors). Note that w ..."
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that we have explicitly included the slack variables s. We will assume rank(A) = m (if not, we can do Gaussian elimination). For now, we will also assume that there exist interior solutions for both problems, i.e. there exist ¯<F
Results 1 - 10
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