![1 Multiplication of two polynomials 2 Alternative FFT algorithm 3 Is](http://s1.studyres.com/store/data/017125821_1-4c4b9a2a2d7a4c68cac9fc59fe729921-300x300.png)
4.5 distributed mutual exclusion
... The ring positions may be allocated in numerical order of network addresses or some other means. It does not matter what the ordering is. All that matters is that each process knows who is next in line after itself. ...
... The ring positions may be allocated in numerical order of network addresses or some other means. It does not matter what the ordering is. All that matters is that each process knows who is next in line after itself. ...
COMPLEXITY - Carlos Eduardo Maldonado
... COMPLEXITY OF PROBLEMS The complexity of a problem is equivalent to the ...
... COMPLEXITY OF PROBLEMS The complexity of a problem is equivalent to the ...
Multivariate classification trees based on minimum features discrete
... data mining context, particularly when dealing with business oriented applications, such as those arising in the frame of customer relationship management. We propose an algorithm for generating decision trees in which multivariate splitting rules are based on the new concept of discrete support vec ...
... data mining context, particularly when dealing with business oriented applications, such as those arising in the frame of customer relationship management. We propose an algorithm for generating decision trees in which multivariate splitting rules are based on the new concept of discrete support vec ...
Summary of big ideas
... • Dynamic programming is a widely-used mathematical technique for solving problems that can be divided into stages and where decisions are required in each stage. • The goal of dynamic programming is to find a combination of decisions that optimizes a certain amount associated with a system. ...
... • Dynamic programming is a widely-used mathematical technique for solving problems that can be divided into stages and where decisions are required in each stage. • The goal of dynamic programming is to find a combination of decisions that optimizes a certain amount associated with a system. ...
Speeding Up HMM Decoding and Training by Exploiting Sequence
... In this section we obtain an Ω( logk n ) speedup for decoding, and a constant speedup in the case where k > log n. We show how to use the LZ78 [22] (henceforth LZ) parsing to find good substrings and how to use the incremental nature of the LZ parse to compute M (W ) for a good substring W in O(k 3 ...
... In this section we obtain an Ω( logk n ) speedup for decoding, and a constant speedup in the case where k > log n. We show how to use the LZ78 [22] (henceforth LZ) parsing to find good substrings and how to use the incremental nature of the LZ parse to compute M (W ) for a good substring W in O(k 3 ...
Low-Level Programming Languages
... data are logically the same and can be stored in the same place • Store, retrieve, and process are actions that the computer can perform on data ...
... data are logically the same and can be stored in the same place • Store, retrieve, and process are actions that the computer can perform on data ...
Overview and History
... if we wanted to write a program to solve Sudoku puzzles, must/should it use the same strategies? ...
... if we wanted to write a program to solve Sudoku puzzles, must/should it use the same strategies? ...
Algorithm
In mathematics and computer science, an algorithm (/ˈælɡərɪðəm/ AL-gə-ri-dhəm) is a self-contained step-by-step set of operations to be performed. Algorithms exist that perform calculation, data processing, and automated reasoning.An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing ""output"" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.The concept of algorithm has existed for centuries, however a partial formalization of what would become the modern algorithm began with attempts to solve the Entscheidungsproblem (the ""decision problem"") posed by David Hilbert in 1928. Subsequent formalizations were framed as attempts to define ""effective calculability"" or ""effective method""; those formalizations included the Gödel–Herbrand–Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's ""Formulation 1"" of 1936, and Alan Turing's Turing machines of 1936–7 and 1939. Giving a formal definition of algorithms, corresponding to the intuitive notion, remains a challenging problem.