  High-energy physics experiments rely on reconstruction of the trajectories of
particles produced at the interaction point. This is a challenging task,
especially in the high track multiplicity environment generated by p-p
collisions at the LHC energies. A typical event includes hundreds of signal
examples (interesting decays) and a significant amount of noise (uninteresting
examples).
  This work describes a modification of the Artificial Retina algorithm for
fast track finding: numerical optimization methods were adopted for fast local
track search. This approach allows for considerable reduction of the total
computational time per event. Test results on simplified simulated model of
LHCb VELO (VErtex LOcator) detector are presented. Also this approach is
well-suited for implementation of paralleled computations as GPGPU which look
very attractive in the context of upcoming detector upgrades.
