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Mathematics and Computers in Simulation
Optimization of Algorithmic Parameters using a Meta-Control Approach*
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Computational Optimization and Applications
Path-following and augmented Lagrangian methods for contact problems in linear elasticity
Journal of Computational and Applied Mathematics
Stopping criteria for inner iterations in inexact potential reduction methods: a computational study
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Automatica (Journal of IFAC)
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Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART II
Computers & Mathematics with Applications
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Mathematics and Computers in Simulation
Computational Optimization and Applications
An aggregate deformation homotopy method for min-max-min problems with max-min constraints
Computational Optimization and Applications
A Subspace Minimization Method for the Trust-Region Step
SIAM Journal on Optimization
On Interior Logarithmic Smoothing and Strongly Stable Stationary Points
SIAM Journal on Optimization
Primal-dual interior-point method for thermodynamic gas-particle partitioning
Computational Optimization and Applications
Structural and Multidisciplinary Optimization
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A Variational Principle for Computing Slow Invariant Manifolds in Dissipative Dynamical Systems
SIAM Journal on Scientific Computing
A primal-dual augmented Lagrangian
Computational Optimization and Applications
Shakedown analysis with multidimensional loading spaces
Computational Mechanics
The application of an oblique-projected Landweber method to a model of supervised learning
Mathematical and Computer Modelling: An International Journal
Manifold learning by preserving distance orders
Pattern Recognition Letters
Fast Design Exploration for Performance, Power and Accuracy Tradeoffs in FPGA-Based Accelerators
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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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 for linear programming was not even contemplated because of the total dominance of the simplex method. Vague but continuing anxiety about barrier methods eventually led to their abandonment in favor of newly emerging, apparently more efficient alternatives such as augmented Lagrangian and sequential quadratic programming methods. By the early 1980s, barrier methods were almost without exception regarded as a closed chapter in the history of optimization. This picture changed dramatically with Karmarkar's widely publicized announcement in 1984 of a fast polynomial-time interior method for linear programming; in 1985, a formal connection was established between his method and classical barrier methods. Since then, interior methods have advanced so far, so fast, that their influence has transformed both the theory and practice of constrained optimization. This article provides a condensed, selective look at classical material and recent research about interior methods for nonlinearly constrained optimization.