Sequential semidefinite optimization for physically and statistically consistent robot identification

Physically and Statistically Based Deformable Models for Medical Image Analysis

Medical imaging continues to permeate the practice of medicine, but automated yet accurate segmentation and labeling of anatomical structures continues to be a major obstacle to computerized medical image analysis. Deformable models, with their roots in estimation theory, optimization, and physics-based dynamical systems, represent a powerful approach to the general problem of medical image seg...

Nonlinear Identification of a Physically Parameterized Robot Model

In the work presented here, a three-step identification procedure for rigid body dynamics, friction, and flexibilities, introduced in (Wernholt and Gunnarsson, 2005), will be utilized and extended. Using the procedure, the parameters can be identified only using motor measurements. In the first step, rigid body dynamics and friction will be identified using a separable least squares method, whe...

Semidefinite Optimization

Local convergence for sequential semidefinite programming

We examine the local convergence of the sequential semidefinite programming algorithmfor solving optimization problems with nonlinear semidefinite constraints. The papers [1, 2]present a proof of local quadratic convergence of this algorithm under a strong assumption ofa second order sufficient optimality condition for a local minimizer. The present work extendslightly the a...

Two theoretical results for sequential semidefinite programming

We examine the local convergence of a sequential semidefinite programming approach for solving nonlinear programs with nonlinear semidefiniteness constraints. Known convergence results are extended to slightly weaker second order sufficient conditions and the resulting subproblems are shown to have local convexity properties that imply a weak form of self-concordance of the barrier subproblems.