A Simple Decomposition Method for Support Vector Machines
Machine Learning
Local convergence analysis of projection-type algorithms: unified approach
Journal of Optimization Theory and Applications
iNEOS: an interactive environment for nonlinear optimization
Applied Numerical Mathematics - Applied and computational mathematics: Selected papers of the third panamerican workshop Trujillo, Peru, 24-28 April 2000
Enriched Methods for Large-Scale Unconstrained Optimization
Computational Optimization and Applications
Large-Scale Active-Set Box-Constrained Optimization Method with Spectral Projected Gradients
Computational Optimization and Applications
Analysis of Nonstationary Time Series Using Support Vector Machines
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
An affine scaling trust-region approach to bound-constrained nonlinear systems
Applied Numerical Mathematics
Some recent advances in projection-type methods for variational inequalities
Journal of Computational and Applied Mathematics - Proceedings of the international conference on recent advances in computational mathematics
Parallel Computing - Special issue: Parallel computing in numerical optimization
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
GALAHAD, a library of thread-safe Fortran 90 packages for large-scale nonlinear optimization
ACM Transactions on Mathematical Software (TOMS)
Computational Optimization and Applications
On Affine-Scaling Interior-Point Newton Methods for Nonlinear Minimization with Bound Constraints
Computational Optimization and Applications
Using the GA and TAO toolkits for solving large-scale optimization problems on parallel computers
ACM Transactions on Mathematical Software (TOMS)
Algorithm 869: ODRPACK95: A weighted orthogonal distance regression code with bound constraints
ACM Transactions on Mathematical Software (TOMS)
Trust region Newton methods for large-scale logistic regression
Proceedings of the 24th international conference on Machine learning
An interior-point affine-scaling trust-region method for semismooth equations with box constraints
Computational Optimization and Applications
Constraint-based fairing of surface meshes
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
A filter-trust-region method for simple-bound constrained optimization
Optimization Methods & Software
A proximal subgradient projection algorithm for linearly constrained strictly convex problems
Optimization Methods & Software
Improving ultimate convergence of an augmented Lagrangian method
Optimization Methods & Software - Dedicated to Professor Michael J.D. Powell on the occasion of his 70th birthday
Modified subspace limited memory BFGS algorithm for large-scale bound constrained optimization
Journal of Computational and Applied Mathematics
Adaptive discrete harmonic grid generation
Mathematics and Computers in Simulation
Discriminant Non-negative Matrix Factorization and Projected Gradients for Frontal Face Verification
Biometrics and Identity Management
An active set quasi-Newton method with projected search for bound constrained minimization
Computers & Mathematics with Applications
Sensitivity analysis of the strain criterion for multidimensional scaling
Computational Statistics & Data Analysis
A sparse counterpart of Reichel and Gragg's package QRUP
Journal of Computational and Applied Mathematics
Nonlinear non-negative component analysis algorithms
IEEE Transactions on Image Processing
An efficient algorithm for a class of fused lasso problems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Smoothness and Convex Area Functionals—Revisited
SIAM Journal on Scientific Computing
ACM Transactions on Mathematical Software (TOMS)
Tackling Box-Constrained Optimization via a New Projected Quasi-Newton Approach
SIAM Journal on Scientific Computing
Estimating Derivatives of Noisy Simulations
ACM Transactions on Mathematical Software (TOMS)
Numerical methods for A-optimal designs with a sparsity constraint for ill-posed inverse problems
Computational Optimization and Applications
An improved GLMNET for L1-regularized logistic regression
The Journal of Machine Learning Research
An active set feasible method for large-scale minimization problems with bound constraints
Computational Optimization and Applications
Pairwise ranking aggregation in a crowdsourced setting
Proceedings of the sixth ACM international conference on Web search and data mining
Measuring the degree of face familiarity based on extended NMF
ACM Transactions on Applied Perception (TAP)
Large-scale linear support vector regression
The Journal of Machine Learning Research
Globally optimal cortical surface matching with exact landmark correspondence
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
An active set truncated Newton method for large-scale bound constrained optimization
Computers & Mathematics with Applications
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We analyze a trust region version of Newton's method for bound-constrained problems. Our approach relies on the geometry of the feasible set, not on the particular representation in terms of constraints. The convergence theory holds for linearly constrained problems and yields global and superlinear convergence without assuming either strict complementarity or linear independence of the active constraints. We also show that the convergence theory leads to an efficient implementation for large bound-constrained problems.