  The recently proposed Hysteretic Optimization (HO) procedure is applied to
the 1D Ising spin chain with long range interactions. To study its
effectiveness, the quality of ground state energies found as a function of the
distance dependence exponent, $\sigma$, is assessed. It is found that the
transition from an infinite-range to a long-range interaction at $\sigma=0.5$
is accompanied by a sharp decrease in the performance . The transition is
signaled by a change in the scaling behavior of the average avalanche size
observed during the hysteresis process. This indicates that HO requires the
system to be infinite-range, with a high degree of interconnectivity between
variables leading to large avalanches, in order to function properly. An
analysis of the way auto-correlations evolve during the optimization procedure
confirm that the search of phase space is less efficient, with the system
becoming effectively stuck in suboptimal configurations much earlier. These
observations explain the poor performance that HO obtained for the
Edwards-Anderson spin glass on finite-dimensional lattices, and suggest that
its usefulness might be limited in many combinatorial optimization problems.
