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Efficient Pseudorandom Generators Based on the DDH Assumption
Efficient Pseudorandom Generators Based on the DDH Assumption

and two-dimensional arrays
and two-dimensional arrays

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mixture densities, maximum likelihood, EM algorithm

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CS 573 Algorithms ¬ Sariel Har-Peled October 16, 2014
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... Pareto vector where fj (x) has maximum value and thus may well be dierent from the other solutions. This is so for the above example MOP. If a few added seeds help much, why not add some more? It is well known [22] that for hard MOPs this may not be possible if weighted sums of the objective functi ...
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... necessarily) of lower dimension than the state vector. The statespace approach is convenient for handling multivariate data and nonlinear/non-Gaussian processes, and it provides a significant advantage over traditional time-series techniques for these problems. A full description is provided in [41] ...
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... xji ≥ 2, ∀S ⊂ N : 2 ≤ |S| ≤ N−2. xij + (i ,j)∈L:i ∈S,j∈N \S ...
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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.
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