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Results 1 - 10
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17,219
A Limited Memory Algorithm for Bound Constrained Optimization
- SIAM JOURNAL ON SCIENTIFIC COMPUTING
, 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based ..."
Abstract
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Cited by 572 (9 self)
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An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based
Interior methods for nonlinear optimization
- SIAM REVIEW
, 2002
"... Interior methods are an omnipresent, conspicuous feature of the constrained optimization landscape today, but it was not always so. Primarily in the form of barrier methods, interior-point techniques were popular during the 1960s for solving nonlinearly constrained problems. However, their use for ..."
Abstract
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Cited by 127 (6 self)
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Interior methods are an omnipresent, conspicuous feature of the constrained optimization landscape today, but it was not always so. Primarily in the form of barrier methods, interior-point techniques were popular during the 1960s for solving nonlinearly constrained problems. However, their use
KNITRO: An integrated package for nonlinear optimization
- Large Scale Nonlinear Optimization, 35–59, 2006
, 2006
"... This paper describes Knitro 5.0, a C-package for nonlinear optimization that combines complementary approaches to nonlinear optimization to achieve robust performance over a wide range of application requirements. The package is designed for solving large-scale, smooth nonlinear programming problems ..."
Abstract
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Cited by 111 (3 self)
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This paper describes Knitro 5.0, a C-package for nonlinear optimization that combines complementary approaches to nonlinear optimization to achieve robust performance over a wide range of application requirements. The package is designed for solving large-scale, smooth nonlinear programming
Particle swarm optimization
, 1995
"... A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear fun ..."
Abstract
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Cited by 3769 (22 self)
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A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear
Survey on Nonlinear Optimization
, 1996
"... In this survey paper, an overview on the different approaches for solving nonlinear optimization problems is given. The presentation includes theory, numeric, interval and symbolic methods. ..."
Abstract
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Cited by 1 (0 self)
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In this survey paper, an overview on the different approaches for solving nonlinear optimization problems is given. The presentation includes theory, numeric, interval and symbolic methods.
SNOPT: An SQP Algorithm For Large-Scale 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
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Cited by 597 (24 self)
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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
A New Extension of the Kalman Filter to Nonlinear Systems
, 1997
"... The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which ..."
Abstract
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Cited by 778 (6 self)
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The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF
Results 1 - 10
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17,219
