The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
In this work, we present an introduction to automatic differentiation,
its use in optimization software, and some new potential usages. We
focus on the potential of this technique in
optimization. We do not dive deeply in the intricacies of automatic
differentiation, but put forward its key ideas. We sketch a survey, as
of today, of automatic differentiation software, but warn the reader
that the situation with respect to software evolves rapidly. In the
last part of the paper, we present some...
A method for solving large convex optimization problems is presented. Such problems usually contain a big linear part and only a small or medium nonlinear part. The parts are tackled using two specialized (and thus efficient) external solvers: purely nonlinear and large-scale linear with a quadratic goal function. The decomposition uses an alteration of projection methods. The construction of the method is based on the zigzagging phenomenon and yields a non-asymptotic convergence, not dependent...
Currently displaying 1 –
11 of
11