Spline-based parallel nonlinear optimization of function sequences

• Presents a scalable algorithm for optimizing sequences of functions in parallel.
• Develops an objective function for modeling nonlinear dynamical system optimization.
• Demonstrates three real-world applications and analyzes the results.
• Performance shows that the algorithm benefits from heterogeneous HPC clusters.
• Two distributed variants of the algorithm are proposed and evaluated.

Nonlinear dynamical system optimization problems exist in many scientific fields, ranging from computer vision to quantitative finance. In these problems, the underlying optimized parameters exhibit a certain degree of continuity, which can be formulated as a discrete sequence of nonlinear functions. Traditionally, such problems are either solved by ad-hoc algorithms or via independent optimization of the underlying functions. The former solutions are difficult to define and develop, requiring expertise in the field, while the latter approach does not take advantage of the inherent sequential properties of the functions. This paper presents a parallel spline-based algorithm for nonlinear optimization of function sequences, with emphasis on dataset sequences that represent dynamically evolving systems. The presented algorithm provides results that are more coherent with fewer evaluations than independent optimization of the sequence functions. We elaborate on the heuristic approach, the motivation behind using splines to model dynamical systems, and the various tiers of concurrency built into the algorithm. Furthermore, we present two distributed variants of the algorithm and compare their convergence with the serial version. The performance of the algorithm is demonstrated on benchmarks and real-world problems in audio signal decomposition, small angle X-ray scattering analysis, and video tracking of arbitrary objects.