Recursive Program Optimization Through Inductive Synthesis Proof Transformation

The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality

Building Ideapreneurship Capability: Delivering Differentiated Customer Value From the Frontline

[Excerpt] Transformational innovation appears to be a dominant aspiration of most leading firms. While such innovation efforts are pervasive across industries and regions, the results from these endeavors can be highly varied. Instead of a single-minded focus on transformational innovation, could incremental innovation, if directly tied to real customer need, be a powerful opportunity for growth and sustainability in this dynamic world? HCL Technologies, a global IT services firm based in Noida, India believes so

Extracting quasi-steady Lagrangian transport patterns from the ocean circulation: An application to the Gulf of Mexico

We construct a climatology of Lagrangian coherent structures (LCSs), the concealed skeleton that shapes transport, with a twelve-year-long data-assimilative simulation of the sea-surface circulation in the Gulf of Mexico (GoM). Computed as time-mean Cauchy-Green strain tensorlines of the climatological velocity, the climatological LCSs (cLCSs) unveil recurrent Lagrangian circulation patterns. cLCSs strongly constrain the ensemble-mean Lagrangian circulation of the instantaneous model velocity, thus we show that a climatological velocity may preserve meaningful transport information. Also, the climatological transport patterns we report agree well with GoM kinematics and dynamics, as described in several previous observational and numerical studies. For example, cLCSs identify regions of persistent isolation, and suggest that coastal regions previously identified as high-risk for pollution impact, are regions of maximal attraction. Also, we show examples where cLCSs are remarkably similar to transport patterns observed during the Deepwater Horizon and Ixtoc oil spills, and during the Grand LAgrangian Deployment (GLAD) experiment. Thus, it is shown that cLCSs are an efficient way of synthesizing vast amounts of Lagrangian information. The cLCS method confirms previous GoM studies, and contributes to our understanding by revealing the persistent nature of the dynamics and kinematics treated therein.Comment: To be submitte