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... – We show that adding a bounded response property while maintaining maximal nondeterminism is NP-hard in the size of the locations of the given timed automaton. – Based on the above result and the NP-hardness of adding two bounded response properties without maximal nondeterminism 2 , we focus on ad ...
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... Able to tell what an algorithm is and have some understanding why we study algorithms ...
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... for synthesizing the synchronization skeleton of programs from their temporal logic specification. More recently, in [5–7], the authors investigate algorithmic methods to locally redesign fault-tolerant programs using their existing fault-intolerant version and a partially available specification. I ...
CS 372: Computational Geometry Lecture 14 Geometric
CS 372: Computational Geometry Lecture 14 Geometric

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108_01_basics

... Able to tell what an algorithm is and have some understanding why we study algorithms ...
LEC01 - aiub study guide
LEC01 - aiub study guide

... getting rid of details, which are affected by specific implementation and hardware ...
Cost-effective Outbreak Detection in Networks Jure Leskovec Andreas Krause Carlos Guestrin
Cost-effective Outbreak Detection in Networks Jure Leskovec Andreas Krause Carlos Guestrin

... that, perhaps counterintuitively, a more cost-effective solution can be obtained, by reading smaller, but higher quality, blogs, which our algorithm can find. There are several possible criteria one may want to optimize in outbreak detection. For example, one criterion seeks to minimize detection time ...
- Wiley Online Library
- Wiley Online Library

A polynomial time algorithm for Rayleigh ratio on
A polynomial time algorithm for Rayleigh ratio on

... problem, all of which are NP-hard. These problems have traditionally been solved, heuristically, using the “spectral technique”. A unified framework is provided here whereby all these problems are formulated as a constrained minimization form of a quadratic ratio, referred to here as the Rayleigh rat ...
An Improved BKW Algorithm for LWE with Applications to
An Improved BKW Algorithm for LWE with Applications to

... BinaryLWE problem with n samples in subexponential time 2(ln 2/2+o(1))n/ log log n . This analysis does not require any heuristic assumption, contrary to other algebraic approaches; instead, it uses a variant of an idea by Lyubashevsky to generate many samples from a small number of samples. This ma ...
Fast Matrix Rank Algorithms and Applications - USC
Fast Matrix Rank Algorithms and Applications - USC

... ci ∈ Fq to represent an element k−1 i=0 ci x in Fq k . The injective mapping f in the statement is just the identity mapping in this construction, i.e. f (c) = (c, 0, 0, . . . , 0). The overall preprocessing time is O(|A| + k2 log2 k log log k(log k + log q)) = O(|A|) field operations in Fqk . It fo ...
An efficient algorithm for the blocked pattern matching problem
An efficient algorithm for the blocked pattern matching problem

... Time complexity ...
Kernel Estimation and Model Combination in A Bandit Problem with
Kernel Estimation and Model Combination in A Bandit Problem with

... Different variants of the bandit problem motivated by real applications have been studied extensively in the past decade. One promising setting is to assume that the reward distribution of each bandit arm is associated with some common external covariate. More specifically, for an l-armed bandit pro ...
Random Walk With Continuously Smoothed Variable Weights
Random Walk With Continuously Smoothed Variable Weights

... weights are increased additively by 1, and smoothed multiplicatively every 50 iterations by 0.8. We compare this with continuous smoothing using s = 0.02. The simulations are shown in Figures 3 and 4. The results are qualitatively similar: both methods adjust weight rankings to new situations in alm ...
A Market-Based Study of Optimal ATM`S Deployment Strategy
A Market-Based Study of Optimal ATM`S Deployment Strategy

The Range 1 Query (R1Q) Problem
The Range 1 Query (R1Q) Problem

... Query Execution. To answer R1QA (i, j), we consider two cases: (1) Intra-word queries: If (i, j) lies inside one word, we answer R1Q using bit shifts. (2) Interword queries: If (i, j) spans multiple words, then the query gets split into three subqueries: (a) R1Q from i to the end of its word, (b) R1 ...
Design of Cognitive Radio Systems Under Temperature
Design of Cognitive Radio Systems Under Temperature

On the Use of Non-Stationary Strategies for Solving Two
On the Use of Non-Stationary Strategies for Solving Two

... stationary strategies. Moreover, this bound has been shown to be tight for MDPs in [15]. Since MDPs are a subclass of MGs, the L∞ -norm bound is also tight for MGs. The VI algorithm presented above produces a sequence of values v0 , ... , vk and, implicitly, strategies μ0 , ... , μk . The non-statio ...
STP A Decision Procedure for Bit
STP A Decision Procedure for Bit

26 Optimal Bounds for Johnson-Lindenstrauss
26 Optimal Bounds for Johnson-Lindenstrauss

... norm, but then have to take a median of several estimators in order to reduce the failure probability. This is inherently nonlinear but suggests the power of such schemes in addressing sparsity as a goal; in contrast, a single transform with constant sparsity per column fails to be an (ε, δ)-JL tran ...
Recent Progress on the Complexity of Solving Markov Decision
Recent Progress on the Complexity of Solving Markov Decision

... To evaluate a given policy π ∈ ΠR , a criterion f is selected, which assigns a number f (x, π) to each initial state x ∈ X. The policy π ∗ is optimal at state x if f (x, π ∗ ) = supπ∈ΠR f (x, π); an optimal policy is optimal at every initial state. Two commonly used criteria are the infinite-horizo ...
+ n
+ n

... It cannot be investigated the way the previous examples are. The halving game: Find integer i such that n/2i ≤ 1. Answer: i ≤ log n. ...
Computing intersections in a set of line segments: the Bentley
Computing intersections in a set of line segments: the Bentley

... intersection among the dead segments has appeared as minimum element in the X-structure. If this claim is true, then the third if-then statement in the algorithm in Figure 1 implies that all intersections among the dead segments have been reported. (Each such intersection is reported in an execution ...
Best Keyword Cover Search
Best Keyword Cover Search

... dimension. Objects close to each other geographically may have very different ratings and thus they are in different nodes of KRR*-tree. If more weight is assigned to keyword ratings, KRR*-tree tends to have more pruning power by distinguishing the objects close ...
International Electrical Engineering Journal (IEEJ)
International Electrical Engineering Journal (IEEJ)

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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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