A merit function approach to the subgradient method with averaging
Optimization Methods & Software
Transfer learning from multiple source domains via consensus regularization
Proceedings of the 17th ACM conference on Information and knowledge management
Reconstructing (0,1)-matrices from projections using integer programming
Computational Optimization and Applications
Vehicle routing problem with fuzzy time windows
Fuzzy Sets and Systems
Image thresholding by variational minimax optimization
Pattern Recognition
Cutting-set methods for robust convex optimization with pessimizing oracles
Optimization Methods & Software
Optimal estimation of deterioration from diagnostic image sequence
IEEE Transactions on Signal Processing
The multiple pairs SMO: a modified SMO algorithm for the acceleration of the SVM training
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A parallel interior point decomposition algorithm for block angular semidefinite programs
Computational Optimization and Applications
Exposing digital forgeries through specular highlights on the eye
IH'07 Proceedings of the 9th international conference on Information hiding
Theoretical analysis of evolutionary computation on continuously differentiable functions
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Profile-driven regression for modeling and runtime optimization of mobile networks
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Estimating multiple frequency-hopping signal parameters via sparse linear regression
IEEE Transactions on Signal Processing
SIAM Journal on Scientific Computing
Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition
Operations Research
Consistency and asymptotic normality of FastICA and bootstrap FastICA
Signal Processing
Calibration of estimator-weights via semismooth Newton method
Journal of Global Optimization
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Image and Vision Computing
Stochastic Optimization of Sensor Placement for Diver Detection
Operations Research
Dense map inference with user-defined priors: from priorlets to scan eigenvariations
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
Journal of Global Optimization
A Bayesian approach to stochastic root finding
Proceedings of the Winter Simulation Conference
Assessing the Value of Dynamic Pricing in Network Revenue Management
INFORMS Journal on Computing
Reasoning about uncertain information and conflict resolution through trust revision
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Optimal reconstruction of material properties in complex multiphysics phenomena
Journal of Computational Physics
Recurrent networks for compressive sampling
Neurocomputing
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Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects, Nonlinear Optimization is the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates the theory and the methods of nonlinear optimization in a unified, clear, and mathematically rigorous fashion, with detailed and easy-to-follow proofs illustrated by numerous examples and figures. The book covers convex analysis, the theory of optimality conditions, duality theory, and numerical methods for solving unconstrained and constrained optimization problems. It addresses not only classical material but also modern topics such as optimality conditions and numerical methods for problems involving nondifferentiable functions, semidefinite programming, metric regularity and stability theory of set-constrained systems, and sensitivity analysis of optimization problems. Based on a decade's worth of notes the author compiled in successfully teaching the subject, this book will help readers to understand the mathematical foundations of the modern theory and methods of nonlinear optimization and to analyze new problems, develop optimality theory for them, and choose or construct numerical solution methods. It is a must for anyone seriously interested in optimization.