Counting | EPFL Graph Search

Counting is the process of determining the number of elements of a finite set of objects; that is, determining the size of a set. The traditional way of counting consists of continually increasing a (mental or spoken) counter by a unit for every element of the set, in some order, while marking (or displacing) those elements to avoid visiting the same element more than once, until no unmarked elements are left; if the counter was set to one after the first object, the value after visiting the final object gives the desired number of elements. The related term enumeration refers to uniquely identifying the elements of a finite (combinatorial) set or infinite set by assigning a number to each element. Counting sometimes involves numbers other than one; for example, when counting money, counting out change, "counting by twos" (2, 4, 6, 8, 10, 12, ...), or "counting by fives" (5, 10, 15, 20, 25, ...). There is archaeological evidence suggesting that humans have been counting for

Computational Aspects of Jacobians of Hyperelliptic Curves

Alina Dudeanu

Nowadays, one area of research in cryptanalysis is solving the Discrete Logarithm Problem (DLP) in finite groups whose group representation is not yet exploited. For such groups, the best one can do is using a generic method to attack the DLP, the fastest of which remains the Pollard rho algorithm with

r

-adding walks. For the first time, we rigorously analyze the Pollard rho method with

r

-adding walks and prove a complexity bound that differs from the birthday bound observed in practice by a relatively small factor. There exist a multitude of open questions in genus

2

cryptography. In this case, the DLP is defined in large prime order subgroups of rational points that are situated on the Jacobian of a genus~

2

curve defined over a large characteristic finite field. We focus on one main topic, namely we present a new efficient algorithm for computing cyclic isogenies between Jacobians. Comparing to previous work that computes non cyclic isogenies in genus~

2

, we need to restrict to certain cases of polarized abelian varieties with specific complex multiplication and real multiplication. The algorithm has multiple applications related to the structure of the isogeny graph in genus~

2

, including random self-reducibility of DLP. It helps support the widespread intuition of choosing \emph{any} curve in a class of curves that satisfy certain public and well studied security parameters. Another topic of interest is generating hyperelliptic curves for cryptographic applications via the CM method that is based on the numerical estimation of the rational Igusa class polynomials. A recent development relates the denominators of the Igusa class polynomials to counting ideal classes in non maximal real quadratic orders whose norm is not prime to the conductor. Besides counting, our new algorithm provides precise representations of such ideal classes for all real quadratic fields and is part of an implementation in Magma of the recent theoretic work in the literature on the topic of denominators.

EPFL2016

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

Damian Mateusz Straszak

Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most sophisticated tasks involving decision making, can be reduced to solving certain optimization problems. These advances however, bring several new challenges to the field of algorithm design. The first of them is related to the ever-growing size of instances, these optimization problems need to be solved for. In practice, this forces the algorithms for these problems to run in time linear or nearly linear in their input size. The second challenge is related to the emergence of new, harder and harder problems which need to be dealt with. These problems are in most cases considered computationally intractable because of complexity barriers such as NP completeness, or because of non-convexity. Therefore, efficiently computable relaxations for these problems are typically desired. The material of this thesis is divided into two parts. In the first part we attempt to address the first challenge. The recent tremendous progress in developing fast algorithm for such fundamental problems as maximum flow or linear programming, demonstrate the power of continuous techniques and tools such as electrical flows, fast Laplacian solvers and interior point methods. In this thesis we study new algorithms of this type based on continuous dynamical systems inspired by the study of a slime mold Physarum polycephalum. We perform a rigorous mathematical analysis of these dynamical systems and extract from them new, fast algorithms for problems such as minimum cost flow, linear programming and basis pursuit. In the second part of the thesis we develop new tools to approach the second challenge. Towards this, we study a very general form of discrete optimization problems and its extension to sampling and counting, capturing a host of important problems such as counting matchings in graphs, computing permanents of matrices or sampling from constrained determinantal point processes. We present a very general framework, based on polynomials, for dealing with these problems computationally. It is based, roughly, on encoding the problem structure in a multivariate polynomial and then recovering the solution by means of certain continuous relaxations. This leads to several questions on how to reason about such relaxations and how to compute them. We resolve them by relating certain analytic properties of the arising polynomials, such as the location of their roots or convexity, to the combinatorial structure of the underlying problem. We believe that the ideas and mathematical techniques developed in this thesis are only a beginning and they will inspire more work on the use of dynamical systems and polynomials in the design of fast algorithms.

EPFL2018