An Infeasible Primal-Dual Interior-Point Algorithm for Linearly Constrained Convex Optimization Based on a Parametric Kernel Function.
Guoqiang WangBaocun WangQingduan FanPublished in: CSO (2) (2009)
Keyphrases
- convex optimization
- primal dual
- kernel function
- interior point algorithm
- variational inequalities
- interior point methods
- support vector
- low rank
- kernel methods
- support vector machine
- kernel matrix
- feature space
- input space
- total variation
- convex sets
- support vectors
- high dimensional
- semidefinite programming
- convex relaxation
- feature set
- linear constraints
- hyperplane
- simplex method
- kernel learning
- machine learning
- denoising
- training data
- image segmentation