Subquadratic Space Research Articles

Toeplitz Matrix Approach for Binary Field Multiplication Using Quadrinomials

In the recent past, subquadratic space complexity multipliers have been proposed for binary fields defined by irreducible trinomials and some specific pentanomials. For such multipliers, alternative irreducible polynomials can also be used, in particular, nearly all one polynomials (NAOPs) seem to be better than pentanomials. For improved efficiency, multiplication modulo an NAOP is performed via modulo a quadrinomial whose degree is one more than that of the original NAOP. In this paper, we present a Toeplitz matrix-vector product based approach for multiplication modulo a quadrinomial. We obtain a fully parallel multiplier with a subquadratic space complexity. The Toeplitz matrix-vector product-based approach is also interesting in the design of sequential multipliers. We present two such multipliers that process a two-bit digit every clock cycle. Field-programmable gate-array implementations of the proposed sequential as well as fully parallel multipliers for the field size of 163 are also presented.

Block Recombination Approach for Subquadratic Space Complexity Binary Field Multiplication Based on Toeplitz Matrix-Vector Product

In this paper, we present a new method for parallel binary finite field multiplication which results in subquadratic space complexity. The method is based on decomposing the building blocks of the Fan-Hasan subquadratic Toeplitz matrix-vector multiplier. We reduce the space complexity of their architecture by recombining the building blocks. In comparison to other similar schemes available in the literature, our proposal presents a better space complexity while having the same time complexity. We also show that block recombination can be used for efficient implementation of the GHASH function of Galois Counter Mode (GCM).

Subquadratic Space Complexity Binary Field Multiplier Using Double Polynomial Representation

This paper deals with binary field multiplication. We use the bivariate representation of binary field called Double Polynomial System (DPS) presented in . This concept generalizes the composite field representation to every finite field. As shown in , the main interest of DPS representation is that it enables to use Lagrange approach for multiplication, and in the best case, Fast Fourier Transform approach, which optimizes Lagrange approach. We use here a different strategy from to perform reduction, and we also propose in this paper, some new approaches for constructing DPS. We focus on DPS, which provides a simpler and more efficient method for coefficient reduction. This enables us to avoid a multiplication required in the Montgomery reduction approach of , and thus to improve the complexity of the DPS multiplier. The resulting algorithm proposed in the present paper is subquadratic in space O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.31</sup> ) and logarithmic in time. The space complexity is 33 percent better than in and 18 percent faster. It is asymptotically more efficient than the best known method (specifiably more efficient than when n ≥ 3,000). Furthermore, our proposal is available for every n and not only for n a power of two or three.

Alternative to the Karatsuba algorithm for software implementations of GF(2n) multiplications

A new approach to subquadratic space complexity GF(2n) multipliers has been proposed recently. The corresponding algorithm for software implementations is developed. While its recursive implementation is as simple as that of the Karatsuba algorithm, it requires much less memory to store the look-up table. Therefore it is quite suitable for memory-constrained applications, for example smart cards.

Range mode and range median queries in constant time and sub-quadratic space

Given a list of n items and a function defined over sub-lists, we study the space required for computing the function for arbitrary sub-lists in constant time. For the function mode we improve the previously known space bound O ( n 2 / log n ) to O ( n 2 log log n / log 2 n ) words. For median the space bound is improved to O ( n 2 log log 2 n / log 2 n ) words from O ( n 2 ⋅ log ( k ) n / log n ) , where k is an arbitrary constant and log ( k ) is the iterated logarithm.

Computing closest and farthest points for a query segment

In this paper we present an improved algorithm for finding k closest (farthest) points for a given arbitrary query segment. We show how to preprocess a planar set P of n given points in O ( n 2 log n ) expected time (or, alternatively, in O ( n 2 log 2 n ) deterministic time) and a subquadratic space, in order to report k closest points to an arbitrary given query line segment in O ( k + log 2 n log log n ) time. Here, for the first time, the data structure that provides polylogarithmic query time and uses subquadratic space is presented. We also show an algorithm for reporting the k farthest points from an arbitrary given query line segment.

A New Approach to Subquadratic Space Complexity Parallel Multipliers for Extended Binary Fields

Based on Toeplitz matrix-vector products and coordinate transformation techniques, we present a new scheme for subquadratic space complexity parallel multiplication in GF(2n) using the shifted polynomial basis. Both the space complexity and the asymptotic gate delay of the proposed multiplier are better than those of the best existing subquadratic space complexity parallel multipliers. For example, with n being a power of 2, the space complexity is about 8 percent better, while the asymptotic gate delay is about 33 percent better, respectively. Another advantage of the proposed matrix-vector product approach is that it can also be used to design subquadratic space complexity polynomial, dual, weakly dual, and triangular basis parallel multipliers. To the best of our knowledge, this is the first time that subquadratic space complexity parallel multipliers are proposed for dual, weakly dual, and triangular bases. A recursive design algorithm is also proposed for efficient construction of the proposed subquadratic space complexity multipliers. This design algorithm can be modified for the construction of most of the subquadratic space complexity multipliers previously reported in the literature

Approximate distance oracles for unweighted graphs in expected O ( n 2 ) time

Let G = ( V , E ) be an undirected graph on n vertices, and let δ( u , v ) denote the distance in G between two vertices u and v . Thorup and Zwick showed that for any positive integer t , the graph G can be preprocessed to build a data structure that can efficiently report t -approximate distance between any pair of vertices. That is, for any u , v ∈ V , the distance reported is at least δ( u , v ) and at most t δ( u , v ). The remarkable feature of this data structure is that, for t ≥3, it occupies subquadratic space, that is, it does not store all-pairs distances explicitly, and still it can answer any t -approximate distance query in constant time. They named the data structure “approximate distance oracle” because of this feature. Furthermore, the trade-off between the stretch t and the size of the data structure is essentially optimal.In this article, we show that we can actually construct approximate distance oracles in expected O ( n 2 ) time if the graph is unweighted. One of the new ideas used in the improved algorithm also leads to the first expected linear-time algorithm for computing an optimal size (2, 1)-spanner of an unweighted graph. A (2, 1) spanner of an undirected unweighted graph G = ( V , E ) is a subgraph ( V , Ê), Ê ⊆ E , such that for any two vertices u and v in the graph, their distance in the subgraph is at most 2δ( u , v ) + 1.

A generalized method for constructing subquadratic complexity GF(2/sup k/) multipliers

We introduce a generalized method for constructing subquadratic complexity multipliers for even characteristic field extensions. The construction is obtained by recursively extending short convolution algorithms and nesting them. To obtain the short convolution algorithms, the Winograd short convolution algorithm is reintroduced and analyzed in the context of polynomial multiplication. We present a recursive construction technique that extends any d point multiplier into an n=d/sup k/ point multiplier with area that is subquadratic and delay that is logarithmic in the bit-length n. We present a thorough analysis that establishes the exact space and time complexities of these multipliers. Using the recursive construction method, we obtain six new constructions, among which one turns out to be identical to the Karatsuba multiplier. All six algorithms have subquadratic space complexities and two of the algorithms have significantly better time complexities than the Karatsuba algorithm.

Time and space bounds for reversible simulation

We prove a general upper bound on the trade-off between time and space that suffices for the reversible simulation of irreversible computation. Previously, only simulations using exponential time or quadratic space were known. The trade-off shows for the first time that we can simultaneously achieve subexponential time and subquadratic space. The boundary values are the exponential time with hardly any extra space required by the Lange–McKenzie–Tapp method and the (log 3)th power time with square space required by the Bennett method. We also give the first general lower bound on the extra storage space required by general reversible simulation. This lower bound is optimal in that it is achieved by some reversible simulations.

Implicitly representing arrangements of lines or segments

An arrangement of n lines (or line segments) in the plane is the partition of the plane defined by these objects. Such an arrangement consists of O(n2) regions, called faces. In this paper we study the problem of calculating and storing arrangements implicitly, using subquadratic space and preprocessing, so that, given any query point p, we can calculate efficiently the face containing p. First, we consider the case of lines and show that with L(n) space1 and L(n3/2) preprocessing time, we can answer face queries in L(√n) + O(K) time, where K is the output size. (The query time is achieved with high probability.) In the process, we solve three interesting subproblems: 1) given a set of n points, find a straight-edge spanning tree of these points such that any line intersects only a few edges of the tree, 2) given a simple polygonal path G, form a data structure from which we can find the convex hull of any subpath of G quickly, and 3) given a set of points, organize them so that the convex hull of their subset lying above a query line can be found quickly. Second, using random sampling, we give a trade-off between increasing space and decreasing query time. Third, we extend our structure to report faces in an arrangement of line segments in L(n1/3) time, given L(n4/3) space and L(n5/3) preprocessing time.Lastly, we note that our techniques allow us to compute m faces in an arrangement of n lines in time L(m2/3n2/3 + n), which is nearly optimal.