Exploring Local Optima in Schematic Layout

In search-based graph drawing methods there are typically a number of parameters that control the search algorithm. These parameters do not affect the ?tness function, but nevertheless have an impact on the ?nal layout. One such search method is hill climbing, and, in the context of schematic layout, we explore how varying three parameters (grid spacing, the starting distance of allowed node movement and the number of iterations) affects the resultant diagram. Although we cannot characterize schematics completely and so cannot yet automatically assign parameters for diagrams, we observe that when parameters are set to values that increase the search space, they also tend to improve the ?nal layout. We come to the conclusion that hillclimbing methods for schematic layout are more prone to reaching local optima than had previously been expected and that a wider search, as described in this paper, can mitigate this, so resulting in a better layout

Performance Guarantees of Local Search for Multiprocessor Scheduling

Increasing interest has recently been shown in analyzing the worst-case behavior of local search algorithms. In particular, the quality of local optima and the time needed to find the local optima by the simplest form of local search has been studied. This paper deals with worst-case performance of local search algorithms for makespan minimization on parallel machines. We analyze the quality of the local optima obtained by iterative improvement over the jump, swap, multi-exchange, and the newly defined push neighborhoods. Finally, for the jump neighborhood we provide bounds on the number of local search steps required to find a local optimum.operations research and management science;

Efficient approaches for escaping higher order saddle points in non-convex optimization

Local search heuristics for non-convex optimizations are popular in applied machine learning. However, in general it is hard to guarantee that such algorithms even converge to a local minimum, due to the existence of complicated saddle point structures in high dimensions. Many functions have degenerate saddle points such that the first and second order derivatives cannot distinguish them with local optima. In this paper we use higher order derivatives to escape these saddle points: we design the first efficient algorithm guaranteed to converge to a third order local optimum (while existing techniques are at most second order). We also show that it is NP-hard to extend this further to finding fourth order local optima