A Still Simpler Way of Introducing the Interior-Point Method for Linear Programming

Linear programming is now included in algorithm undergraduate and postgraduate courses for computer science majors. We give a self-contained treatment of an interior-point method which is particularly tailored to the typical mathematical background of CS students. In particular, only limited knowledge of linear algebra and calculus is assumed.Comment: Updates and replaces arXiv:1412.065

Computing Real Roots of Real Polynomials

Computing the roots of a univariate polynomial is a fundamental and long-studied problem of computational algebra with applications in mathematics, engineering, computer science, and the natural sciences. For isolating as well as for approximating all complex roots, the best algorithm known is based on an almost optimal method for approximate polynomial factorization, introduced by Pan in 2002. Pan's factorization algorithm goes back to the splitting circle method from Schoenhage in 1982. The main drawbacks of Pan's method are that it is quite involved and that all roots have to be computed at the same time. For the important special case, where only the real roots have to be computed, much simpler methods are used in practice; however, they considerably lag behind Pan's method with respect to complexity. In this paper, we resolve this discrepancy by introducing a hybrid of the Descartes method and Newton iteration, denoted ANEWDSC, which is simpler than Pan's method, but achieves a run-time comparable to it. Our algorithm computes isolating intervals for the real roots of any real square-free polynomial, given by an oracle that provides arbitrary good approximations of the polynomial's coefficients. ANEWDSC can also be used to only isolate the roots in a given interval and to refine the isolating intervals to an arbitrary small size; it achieves near optimal complexity for the latter task.Comment: to appear in the Journal of Symbolic Computatio

The Cost of Address Translation

Modern computers are not random access machines (RAMs). They have a memory hierarchy, multiple cores, and virtual memory. In this paper, we address the computational cost of address translation in virtual memory. Starting point for our work is the observation that the analysis of some simple algorithms (random scan of an array, binary search, heapsort) in either the RAM model or the EM model (external memory model) does not correctly predict growth rates of actual running times. We propose the VAT model (virtual address translation) to account for the cost of address translations and analyze the algorithms mentioned above and others in the model. The predictions agree with the measurements. We also analyze the VAT-cost of cache-oblivious algorithms.Comment: A extended abstract of this paper was published in the proceedings of ALENEX13, New Orleans, US

Arbitrary weight changes in dynamic trees

We describe an implementation of dynamic weighted trees, called D-trees. Given a set left{ B_{0},...,B_{n}right} of objects and access frequencies q_{0},q_{1},...,q_{n} one wants to store the objects in a binary tree such that average access is nearly optimal and changes of the access frequencies require only small changes of the tree. In D-trees the changes are always limited to the path of search and hence update time is at most proportional to search time

Cache-Oblivious VAT-Algorithms

The VAT-model (virtual address translation model) extends the EM-model (external memory model) and takes the cost of address translation in virtual memories into account. In this model, the cost of a single memory access may be logarithmic in the largest address used. We show that the VAT-cost of cache-oblivious algorithms is only by a constant factor larger than their EM-cost; this requires a somewhat more stringent tall cache assumption as for the EM-model

An efficient algorithm for constructing nearly optimal prefix codes

A new algorithm for constructing nearly optimal prefix codes in the case of unequal letter costs and unequal probabilities is presented. A bound on the maximal deviation from the optimum is derived and numerical examples are given. The algorithm has running time O(t·n) where t is the number of letters and n is the number of probabilities

Sorting presorted files

A new sorting algorithm is presented. Its running time is O(n(1+10g(F/n)) where F = I{(f,j); i < j and xi < xj}1 is the total number of inversions in the input sequence xn xn_1 xn_2 ... x2 xl• In other words, presorted sequences are sorted quickly, and completely unsorted sequences are sorted in O(n log n) steps. Note that F < n2/2 always. Furthermore, the constant of proportionality is fairly small and hence the sorting method is competitive with existing methods for not too large n

Engineering DFS-Based Graph Algorithms

Depth-first search (DFS) is the basis for many efficient graph algorithms. We introduce general techniques for the efficient implementation of DFS-based graph algorithms and exemplify them on three algorithms for computing strongly connected components. The techniques lead to speed-ups by a factor of two to three compared to the implementations provided by LEDA and BOOST. We have obtained similar speed-ups for biconnected components algorithms. We also compare the graph data types of LEDA and BOOST