System-on-Chip (SoC) architectures integrate several heterogeneous components onto a single chip. These components provide various capabilities such as dynamic voltage scaling, reconfiguration, multiple power states, etc. that can be exploited for performance optimization during application design. We propose a Generic Model (GenM) which captures the capabilities of a large class of SoC archite...

Use of heuristic methods is common to find the solutions to the optimization problems for scientific and real time. Problems such as Travelling Salesman (TSP) require more accurate solution which is tried by various optimization methods. Research in this direction shows the use of Genetic algorithms (GA) as promising candidate and is preferred over other optimization methods. Firstly due to the...

Leaf vascular patterns are the mechanisms and mechanical support for the transportation of fluidics for photosynthesis and leaf development properties. Vascular hierarchical networks in leaves have far-reaching functions in optimal transport efficiency of functional fluidics. Embedding leaf morphogenesis as a resistor network is significant in the optimization of a translucent thermally functio...

This paper proposes a new algorithm called soft partitioning particle swarm optimization (SPPSO), which performs video-based markerless human pose tracking by optimizing a fitness function in a 31-dimensional search space. The fitness function is based on foreground segmentation and edges. SPPSO divides the optimization into two stages that exploit the hierarchical structure of the model. The f...

We propose a new class of convex penalty functions, called variational Gram functions (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vectors in a vector space. These functions can serve as regularizers in convex optimization problems arising from hierarchical classification, multitask learning, and estimating vectors with disjoint supports, among other applic...

I. Introduction Multi-level programming is a powerful technique for solving hierarchical decision-making problems.Multi-level optimization plays an important role in engineering design, management, and decision making in general. Ultimately, a designer or decision maker needs to make tradeoffs between disparate and conflicting design objectives. The field of multi-level optimization defines the...

In this paper we propose the use of optimization based clustering algorithms to determine hierarchical multicast trees. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artificial and real-world databases are reported. We compare several ...

Bilevel programs form a class of hierarchical optimzation problems in which the constraint set is not explicit but is defined in terms of another optimization problem. Optimization problems in several real-life domains can be expressed as bilevel programs, making such problems ubiquitous. In this term paper, we analyze a trust region approach for solving a special case of this problem.

A hierarchical approach to robust design optimization considering the needs of robust performance from multiple disciplines is proposed in this paper. The approach extends the framework of Collaborative Optimization to perform robust design with multiple objectives from different disciplines under uncertainty. The proposed approach is demonstrated by examples and compared with the all-in-one ap...

This paper presents the latest advances we made in static and dynamic locomotion with our compliant quadrupedal robot StarlETH. We outline a hierarchical task-space inverse dynamics framework based on least square optimization that allows for simple generation of complex robot behaviors and simultaneous optimization of joint torque and contact force objectives. Strong focus is put on experiment...