Journal of Computational and Applied Mathematics
Direct boolean intersection between acquired and designed geometry
Computer-Aided Design
Defining, contouring, and visualizing scalar functions on point-sampled surfaces
Computer-Aided Design
Constrained logistic regression for discriminative pattern mining
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
EuroHaptics'12 Proceedings of the 2012 international conference on Haptics: perception, devices, mobility, and communication - Volume Part I
Hi-index | 0.00 |
We propose a new trust region approach for minimizing nonlinear functions subject to simple bounds. By choosing an appropriate quadratic model and scaling matrix at each iteration, we show that it is not necessary to solve a quadratic programming subproblem, with linear inequalities, to obtain an improved step using the trust region idea. Instead, a solution to a trust region subproblem is defined by minimizing a quadratic function subject only to an ellipsoidal constraint. The iterates generated by these methods are always strictly feasible. Our proposed methods reduce to a standard trust region approach for the unconstrained problem when there are no upper or lower bounds on the variables. Global and quadratic convergence of the methods is established; preliminary numerical experiments are reported.