SuperoptimizationSuperoptimization is the process where a compiler automatically finds the optimal sequence for a loop-free sequence of instructions. Real-world compilers generally cannot produce genuinely optimal code, and while most standard compiler optimizations only improve code partly, a superoptimizer's goal is to find the optimal sequence, the canonical form. Superoptimizers can be used to improve conventional optimizers by highlighting missed opportunities so a human can write additional rules.
Amortized analysisIn computer science, amortized analysis is a method for analyzing a given algorithm's complexity, or how much of a resource, especially time or memory, it takes to execute. The motivation for amortized analysis is that looking at the worst-case run time can be too pessimistic. Instead, amortized analysis averages the running times of operations in a sequence over that sequence. As a conclusion: "Amortized analysis is a useful tool that complements other techniques such as worst-case and average-case analysis.
Master of PhilosophyA Master of Philosophy (MPhil; Latin Magister Philosophiae or Philosophiae Magister) is a postgraduate degree. An MPhil may be awarded to postgraduate students after completing taught coursework and one to two years of original research, which may also serve as a provisional enrolment for a PhD programme. In the United States, an MPhil typically includes a taught portion and a significant research portion, during which a thesis project is conducted under supervision.
Disjoint-set data structureIn computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that stores a collection of disjoint (non-overlapping) sets. Equivalently, it stores a partition of a set into disjoint subsets. It provides operations for adding new sets, merging sets (replacing them by their union), and finding a representative member of a set. The last operation makes it possible to find out efficiently if any two elements are in the same or different sets.
Maximally stable extremal regionsIn computer vision, maximally stable extremal regions (MSER) are used as a method of blob detection in images. This technique was proposed by Matas et al. to find correspondences between image elements from two images with different viewpoints. This method of extracting a comprehensive number of corresponding image elements contributes to the wide-baseline matching, and it has led to better stereo matching and object recognition algorithms. Image is a mapping .
