No Arabic abstract
Let $mathbb{F}[X]$ be the polynomial ring over the variables $X={x_1,x_2, ldots, x_n}$. An ideal $I=langle p_1(x_1), ldots, p_n(x_n)rangle$ generated by univariate polynomials ${p_i(x_i)}_{i=1}^n$ is a emph{univariate ideal}. We study the ideal membership problem for the univariate ideals and show the following results. item Let $f(X)inmathbb{F}[ell_1, ldots, ell_r]$ be a (low rank) polynomial given by an arithmetic circuit where $ell_i : 1leq ileq r$ are linear forms, and $I=langle p_1(x_1), ldots, p_n(x_n)rangle$ be a univariate ideal. Given $vec{alpha}in {mathbb{F}}^n$, the (unique) remainder $f(X) pmod I$ can be evaluated at $vec{alpha}$ in deterministic time $d^{O(r)}cdot poly(n)$, where $d=max{deg(f),deg(p_1)ldots,deg(p_n)}$. This yields an $n^{O(r)}$ algorithm for minimum vertex cover in graphs with rank-$r$ adjacency matrices. It also yields an $n^{O(r)}$ algorithm for evaluating the permanent of a $ntimes n$ matrix of rank $r$, over any field $mathbb{F}$. Over $mathbb{Q}$, an algorithm of similar run time for low rank permanent is due to Barvinok[Bar96] via a different technique. item Let $f(X)inmathbb{F}[X]$ be given by an arithmetic circuit of degree $k$ ($k$ treated as fixed parameter) and $I=langle p_1(x_1), ldots, p_n(x_n)rangle$. We show in the special case when $I=langle x_1^{e_1}, ldots, x_n^{e_n}rangle$, we obtain a randomized $O^*(4.08^k)$ algorithm that uses $poly(n,k)$ space. item Given $f(X)inmathbb{F}[X]$ by an arithmetic circuit and $I=langle p_1(x_1), ldots, p_k(x_k) rangle$, membership testing is $W[1]$-hard, parameterized by $k$. The problem is $MINI[1]$-hard in the special case when $I=langle x_1^{e_1}, ldots, x_k^{e_k}rangle$.
We provide a number of algorithmic results for the following family of problems: For a given binary mtimes n matrix A and integer k, decide whether there is a simple binary matrix B which differs from A in at most k entries. For an integer r, the simplicity of B is characterized as follows. - Binary r-Means: Matrix B has at most r different columns. This problem is known to be NP-complete already for r=2. We show that the problem is solvable in time 2^{O(klog k)}cdot(nm)^{O(1)} and thus is fixed-parameter tractable parameterized by k. We prove that the problem admits a polynomial kernel when parameterized by r and k but it has no polynomial kernel when parameterized by k only unless NPsubseteq coNP/poly. We also complement these result by showing that when being parameterized by r and k, the problem admits an algorithm of running time 2^{O(rcdot sqrt{klog{(k+r)}})}(nm)^{O(1)}, which is subexponential in k for rin O(k^{1/2 -epsilon}) for any epsilon>0. - Low GF(2)-Rank Approximation: Matrix B is of GF(2)-rank at most r. This problem is known to be NP-complete already for r=1. It also known to be W[1]-hard when parameterized by k. Interestingly, when parameterized by r and k, the problem is not only fixed-parameter tractable, but it is solvable in time 2^{O(r^{ 3/2}cdot sqrt{klog{k}})}(nm)^{O(1)}, which is subexponential in k. - Low Boolean-Rank Approximation: Matrix B is of Boolean rank at most r. The problem is known to be NP-complete for k=0 as well as for r=1. We show that it is solvable in subexponential in k time 2^{O(r2^rcdot sqrt{klog k})}(nm)^{O(1)}.
Linear pseudorandom number generators are very popular due to their high speed, to the ease with which generators with a sizable state space can be created, and to their provable theoretical properties. However, they suffer from linear artifacts which show as failures in linearity-related statistical tests such as the binary-rank and the linear-complexity test. In this paper, we give three new contributions. First, we introduce two new linear transformations that have been handcrafted to have good statistical properties and at the same time to be programmable very efficiently on superscalar processors, or even directly in hardware. Then, we describe a new test for Hamming-weight dependencies that is able to discover subtle, previously unknown biases in existing generators (in particular, in linear ones). Finally, we describe a number of scramblers, that is, nonlinear functions applied to the state array that reduce or delete the linear artifacts, and propose combinations of linear transformations and scramblers that give extremely fast pseudorandom generators of high quality. A novelty in our approach is that we use ideas from the theory of filtered linear-feedback shift register to prove some properties of our scramblers, rather than relying purely on heuristics. In the end, we provide simple, extremely fast generators that use a few hundred bits of memory, have provable properties and pass very strong statistical tests.
We study the number of generators of ideals in regular rings and ask the question whether $mu(I)<mu(I^2)$ if $I$ is not a principal ideal, where $mu(J)$ denotes the number of generators of an ideal $J$. We provide lower bounds for the number of generators for the powers of an ideal and also show that the CM-type of $I^2$ is $geq 3$ if $I$ is a monomial ideal of height $n$ in $K[x_1,ldots,x_n]$ and $ngeq 3$.
The NP-hard Multiple Hitting Set problem is finding a minimum-cardinality set intersecting each of the sets in a given input collection a given number of times. Generalizing a well-known data reduction algorithm due to Weihe, we show a problem kernel for Multiple Hitting Set parameterized by the Dilworth number, a graph parameter introduced by Foldes and Hammer in 1978 yet seemingly so far unexplored in the context of parameterized complexity theory. Using matrix multiplication, we speed up the algorithm to quadratic sequential time and logarithmic parallel time. We experimentally evaluate our algorithms. By implementing our algorithm on GPUs, we show the feasability of realizing kernelization algorithms on SIMD (Single Instruction, Multiple Date) architectures.
After the number of vertices, Vertex Cover is the largest of the classical graph parameters and has more and more frequently been used as a separate parameter in parameterized problems, including problems that are not directly related to the Vertex Cover. Here we consider the TREEWIDTH and PATHWIDTH problems parameterized by k, the size of a minimum vertex cover of the input graph. We show that the PATHWIDTH and TREEWIDTH can be computed in O*(3^k) time. This complements recent polynomial kernel results for TREEWIDTH and PATHWIDTH parameterized by the Vertex Cover.