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... joint attached to an articulated body – The problem simplifies to the forward dynamics of a one-joint robot (much simpler than the general case) – The first joint is simply a one-joint robot – The second joint is a one-joint robot with a moving base (slightly more complicated, but still much simpler ...
ppt presentation
ppt presentation

... Input: Polygonal path P, Arbitrary query line l Output: intersections of P & l ...
Building Portfolios for the Protein Structure Prediction
Building Portfolios for the Protein Structure Prediction

... The protein structure prediction problem has been widely studied in the field of bioinformatics, because the 3D conformation of a given protein helps to determine its function. This problem is usually tackled using simplified models such as HP-models in [2] and a constraint logic programming approac ...
Lecture 6 6.1 A RAM Model
Lecture 6 6.1 A RAM Model

... Fixing the model of computation, we can be precise about measuring efficiency: Definition 6.6. The running time of P on an input x ∈ N∗ is the number t of steps before P reaches a halting configuration on x. That is, the smallest t for which there is a sequence C0 ⇒P C1 ⇒P · · · ⇒P Ct such that C0 i ...
DATA STRUCTURES - University of Cape Town
DATA STRUCTURES - University of Cape Town

... Problem 2:Shopping Offers Given a set of items (up to 5) and their individual prices, and a set of special offers (up to 99) : 3 of item A plus 2 of item B for a certain price. Find the minimum cost to purchase a certain amount (up to 5) of each items. Shortest Path Problem ...
Speeding Up HMM Decoding and Training by Exploiting Sequence
Speeding Up HMM Decoding and Training by Exploiting Sequence

Design and implementation of parallel algorithms for highly
Design and implementation of parallel algorithms for highly

... –  The more different P1 and P2, the smaller will be the range of sizes R12 where their relative speed can be accurately approximated by a constant –  If the number of significantly different PUs is large enough, then region ∩Rij of applicability of CPM-based algorithms can be very small ...
LimTiekYeeMFKE2013ABS
LimTiekYeeMFKE2013ABS

Mouse in a Maze - Bowdoin College
Mouse in a Maze - Bowdoin College

... How was the MPG program (Fig 2.5) designed? Problem statement: Write a pseudocode algorithm to compute the distance traveled and the average miles per gallon on a trip when given as input the number of gallons used and the starting and ending mileage readings on the odometer. Input: number of gallo ...
Using Algorithms
Using Algorithms

... Can be thought of as the computer’s “native” language Language is machine-dependent, each type of computer has its own code Every statement in machine language contains an instruction and the data or the location of the data that the instruction will use. Very difficult for humans to use Copyright © ...
mining on car database employing learning and clustering algorithms
mining on car database employing learning and clustering algorithms

... probabilistic classifier with strong (naive) independent assumptions. It can be also more descriptively terms ad “independent feature classifier”. In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is not related to the presence (or ab ...
Efficient Algorithms and Problem Complexity
Efficient Algorithms and Problem Complexity

... The performance ratio is indeed 2, i.e., for some instances, nextFit uses (almost) twice as many bins as is optimal. [Can you find one?] It is an online algorithm: items are processed as they arrive. It is a 1-bounded-space algorithm: at most one bin is open at a time. These are very useful properti ...
Big-O examples
Big-O examples

... algorithm is used, since we would otherwise spend more time than necessary sorting.  The algorithms  discussed can be applied to any type of objects, including integers, floating point numbers, strings, and  complex objects. In general, efficient algorithms are more important than coding tricks.   ...
Using Algorithms
Using Algorithms

... Can be thought of as the computer’s “native” language Language is machine-dependent, each type of computer has its own code Every statement in machine language contains an instruction and the data or the location of the data that the instruction will use. Very difficult for humans to use Copyright © ...
voor dia serie SNS
voor dia serie SNS

PPT
PPT

... Simulate the mapping xy00...0  xyf (x)00...0, (i.e., clean up the “garbage”) To do this, use an additional register and: 1. compute xy00...000...0  xyf (x)g(x) (ignoring the 2nd register in this step) 2. compute xyf (x)g(x)  xyf (x)f (x)g(x) (using CN ...
File
File

... When searching for the number 62, give the value of the middle, upper and lower variables after the second pass. ...
Divide and Conquer
Divide and Conquer

... function DC (x) : answer if length(x) < threshold then return adhoc(x) decompose x into a sub-instances x1, x2 … xa of size n/b for i  1 to a do yi  DC(xi) recombine the yi’s to get a solution y for x return y Where : adhoc(x) = is the basic algorithm for small instances a = the number of division ...
Part A
Part A

APPLICATION OF ORDER STATISTICS TO TERMINATION
APPLICATION OF ORDER STATISTICS TO TERMINATION

... Confidence bounds of the hitting probability ...
Analysis of Algorithms, cont.
Analysis of Algorithms, cont.

... Take the log of both sides ...
Document
Document

...  E.g. sorting problem  the number of items to be sorted  E.g. multiply two matrices together  the total number of elements in the two matrices ...
Deployment of Sensing Devices on Critical Infrastructure
Deployment of Sensing Devices on Critical Infrastructure

... Analysis ...
U.C. Berkeley — CS270: Algorithms Lectures 13, 14 Scribe: Anupam
U.C. Berkeley — CS270: Algorithms Lectures 13, 14 Scribe: Anupam

... The moment F0 counts the number of distinct items, an algorithm that estimates F0 can be used to find number of unique visitors to a website, by processing the stream of ip addresses. The moment F1 is trivial as it is the length of the stream while computing F2 is more involved. The streaming algori ...
L10: k-Means Clustering
L10: k-Means Clustering

... • Smoothed analysis: if data perturbed randomly slightly, then R = O(n35 k 34 d8 ). This is “polynomial,” but still ridiculous. • If all points are on a grid of length M , then R = O(dn4 M 2 ). But thats still way too big. Lesson: there are crazy special cases that can take a long time, but usually ...
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Time complexity

In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the string representing the input. The time complexity of an algorithm is commonly expressed using big O notation, which excludes coefficients and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e., as the input size goes to infinity. For example, if the time required by an algorithm on all inputs of size n is at most 5n3 + 3n for any n (bigger than some n0), the asymptotic time complexity is O(n3).Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, where an elementary operation takes a fixed amount of time to perform. Thus the amount of time taken and the number of elementary operations performed by the algorithm differ by at most a constant factor.Since an algorithm's performance time may vary with different inputs of the same size, one commonly uses the worst-case time complexity of an algorithm, denoted as T(n), which is defined as the maximum amount of time taken on any input of size n. Less common, and usually specified explicitly, is the measure of average-case complexity. Time complexities are classified by the nature of the function T(n). For instance, an algorithm with T(n) = O(n) is called a linear time algorithm, and an algorithm with T(n) = O(Mn) and mn= O(T(n)) for some M ≥ m > 1 is said to be an exponential time algorithm.
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