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Searching worst cases of a onevariable function
 IEEE Transactions on Computers
"... Abstract—We propose a new algorithm to find worst cases for the correct rounding of a mathematical function of one variable. We first reduce this problem to the real small value problem—i.e., for polynomials with real coefficients. Then, we show that this second problem can be solved efficiently by ..."
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Cited by 17 (7 self)
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Abstract—We propose a new algorithm to find worst cases for the correct rounding of a mathematical function of one variable. We first reduce this problem to the real small value problem—i.e., for polynomials with real coefficients. Then, we show that this second problem can be solved efficiently
Worst Cases and Lattice Reduction
"... We propose a new algorithm to find worst cases for correct rounding of an analytic function. We first reduce this problem to the real small value problem — i.e. for polynomials with real coefficients. Then we show that this second problem can be solved efficiently, by extending Coppersmith’s work on ..."
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Cited by 12 (4 self)
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on the integer small value problem — for polynomials with integer coefficients — using lattice reduction [4, 5, 6]. For floatingpoint numbers with a mantissa less than, and a polynomial approximation of ¡ degree, our algorithm finds all worst cases ¢ at distance a machine number �� � § ¥�©������� � in time
Opportunistic Data Structures with Applications
, 2000
"... In this paper we address the issue of compressing and indexing data. We devise a data structure whose space occupancy is a function of the entropy of the underlying data set. We call the data structure opportunistic since its space occupancy is decreased when the input is compressible and this space ..."
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Cited by 296 (11 self)
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and this space reduction is achieved at no significant slowdown in the query performance. More precisely, its space occupancy is optimal in an informationcontent sense because a text T [1, u] is stored using O(H k (T )) + o(1) bits per input symbol in the worst case, where H k (T ) is the kth order empirical
Worstcase to averagecase reductions based on Gaussian measures
 SIAM J. on Computing
, 2004
"... We show that finding small solutions to random modular linear equations is at least as hard as approximating several lattice problems in the worst case within a factor almost linear in the dimension of the lattice. The lattice problems we consider are the shortest vector problem, the shortest indepe ..."
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Cited by 131 (23 self)
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We show that finding small solutions to random modular linear equations is at least as hard as approximating several lattice problems in the worst case within a factor almost linear in the dimension of the lattice. The lattice problems we consider are the shortest vector problem, the shortest
Publickey cryptosystems from the worstcase shortest vector problem
, 2008
"... We construct publickey cryptosystems that are secure assuming the worstcase hardness of approximating the length of a shortest nonzero vector in an ndimensional lattice to within a small poly(n) factor. Prior cryptosystems with worstcase connections were based either on the shortest vector probl ..."
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Cited by 152 (22 self)
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We construct publickey cryptosystems that are secure assuming the worstcase hardness of approximating the length of a shortest nonzero vector in an ndimensional lattice to within a small poly(n) factor. Prior cryptosystems with worstcase connections were based either on the shortest vector
WorstCase to AverageCase Reductions for Module Lattices
"... Abstract. Most latticebased cryptographic schemes are built upon the assumed hardness of the Short Integer Solution (SIS) and Learning With Errors (LWE) problems. Their efficiencies can be drastically improved by switching the hardness assumptions to the more compact RingSIS and RingLWE problems. ..."
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Cited by 6 (1 self)
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lattices (which themselves generalize arbitrary and ideal lattices). As these new problems enlarge the toolbox of the latticebased cryptographer, they could prove useful for designing new schemes. Importantly, the worstcase to averagecase reductions for the module problems are (qualitatively) sharp
Worst case complexity of direct search
, 2010
"... In this paper we prove that direct search of directional type shares the worst case complexity bound of steepest descent when sufficient decrease is imposed using a quadratic function of the step size parameter. This result is proved under smoothness of the objective function and using a framework o ..."
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Cited by 33 (4 self)
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In this paper we prove that direct search of directional type shares the worst case complexity bound of steepest descent when sufficient decrease is imposed using a quadratic function of the step size parameter. This result is proved under smoothness of the objective function and using a framework
Cuckoo hashing
 JOURNAL OF ALGORITHMS
, 2001
"... We present a simple dictionary with worst case constant lookup time, equaling the theoretical performance of the classic dynamic perfect hashing scheme of Dietzfelbinger et al. (Dynamic perfect hashing: Upper and lower bounds. SIAM J. Comput., 23(4):738–761, 1994). The space usage is similar to that ..."
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Cited by 202 (7 self)
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to that of binary search trees, i.e., three words per key on average. Besides being conceptually much simpler than previous dynamic dictionaries with worst case constant lookup time, our data structure is interesting in that it does not use perfect hashing, but rather a variant of open addressing where keys can
Efficient collisionresistant hashing from worstcase assumptions on cyclic lattices
 In TCC
, 2006
"... Abstract The generalized knapsack function is defined as fa(x) = Pi ai * xi, where a = (a1,..., am)consists of m elements from some ring R, and x = (x1,..., xm) consists of m coefficients froma specified subset S ` R. Micciancio (FOCS 2002) proposed a specific choice of the ring R andsubset S for w ..."
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Cited by 61 (16 self)
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S for which inverting this function (for random a, x) is at least as hard as solving certainworstcase problems on cyclic lattices. We show that for a different choice of S ae R, the generalized knapsack function is in factcollisionresistant, assuming it is infeasible to approximate the shortest vector in n
On ideal lattices and learning with errors over rings
 In Proc. of EUROCRYPT, volume 6110 of LNCS
, 2010
"... The “learning with errors ” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount of noise, from truly uniform ones. The problem has been shown to be as hard as worstcase lattice problems, and in recent years it has served as the foundation for a pleth ..."
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Cited by 125 (18 self)
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The “learning with errors ” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount of noise, from truly uniform ones. The problem has been shown to be as hard as worstcase lattice problems, and in recent years it has served as the foundation for a
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