Improved approximation bounds for the minimum rainbow subgraph problem

In this paper we consider the Minimum Rainbow Subgraph problem (MRS): Given a graph G with n vertices whose edges are coloured with p colours, find a subgraph F⊆G of minimum order and with p edges such that F contains each colour exactly once.We present a polynomial time -approximation algorithm for the MRS problem for an arbitrary small positive ?. This improves the previously best known approximation ratio of . We also prove the MRS problem to be NP-hard and APX-hard for graphs with maximum degree 2. Finally we present an algorithm to find an optimal solution in running time O(²(p+2plog₂Δ)nO(1)).     

Testing juntas

We show that a boolean valued function over n variables, where each variable ranges in an arbitrary probability space, can be tested for the property of depending on only J of them using a number of queries that depends only polynomially on J and the approximation parameter ε. We present several tests that require a number of queries that is polynomial in J and linear in ε⁻¹. We show a non-adaptive test that has one-sided error, an adaptive version of it that requires fewer queries, and a non-adaptive two-sided version of the test that requires the least number of queries. We also show a two-sided non-adaptive test that applies to functions over n boolean variables, and has a more compact analysis.We then provide a lower bound of on the number of queries required for the non-adaptive testing of the above property; a lower bound of for adaptive algorithms naturally follows from this. In establishing this lower bound we also prove a result about random walks on the group Z^q₂ that may be interesting in its own right. We show that for some , the distributions of the random walk at times t and t+2 are close to each other, independently of the step distribution of the walk.We also discuss related questions. In particular, when given in advance a known J-junta function , we show how to test a function for the property of being identical to up to a permutation of the variables, in a number of queries that is polynomial in J and ε⁻¹.     

A running time analysis of an Ant Colony Optimization algorithm for shortest paths in directed acyclic graphs

In this paper, we prove polynomial running time bounds for an Ant Colony Optimization (ACO) algorithm for the single-destination shortest path problem on directed acyclic graphs. More specifically, we show that the expected number of iterations required for an ACO-based algorithm with n ants is for graphs with n nodes and m edges, where ρ is an evaporation rate. This result can be modified to show that an ACO-based algorithm for One-Max with multiple ants converges in expected iterations, where n is the number of variables. This result stands in sharp contrast with that of Neumann and Witt, where a single-ant algorithm is shown to require an exponential running time if ρ=O(n−1−ε) for any ε>0.     

On the lower bounds of the second order nonlinearities of some Boolean functions

The rth order nonlinearity of a Boolean function is an important cryptographic criterion in analyzing the security of stream as well as block ciphers. It is also important in coding theory as it is related to the covering radius of the Reed-Muller code R(r,n). In this paper we deduce the lower bounds of the second order nonlinearities of the following two types of Boolean functions:

1.

with d=2^2r+2^r+1 and , where n=6r.

2.

, where x,y∈F_2^t,n=2t,n?6 and i is an integer such that 1?i<t,gcd(2^t-1,2ⁱ+1)=1.

For some λ, the functions of the first type are bent functions, whereas Boolean functions of the second type are all bent functions, i.e., they possess the maximum first order nonlinearity. It is demonstrated that in some cases our bounds are better than the previously obtained bounds.     

How much precision is needed to compare two sums of square roots of integers?

In this paper, we investigate the problem of the minimum nonzero difference between two sums of square roots of integers. Let r(n,k) be the minimum positive value of where ai and bi are integers not larger than integer n. We prove by an explicit construction that r(n,k)=O(n−2k+3/2) for fixed k and any n. Our result implies that in order to compare two sums of k square roots of integers with at most d digits per integer, one might need precision of as many as digits. We also prove that this bound is optimal for a wide range of integers, i.e., r(n,k)=Θ(n−2k+3/2) for fixed k and for those integers in the form of and , where n^′ is any integer satisfied the form and i is any integer in [0,k−1]. We finally show that for k=2 and any n, this bound is also optimal, i.e., r(n,2)=Θ(n−7/2).