Independent domination versus weighted independent domination.

Independent domination is one of the rare problems for which the complexity of weighted and unweighted versions is known to be different in some classes of graphs. Trying to better understand the gap between the two versions of the problem, in the present paper we prove two complexity results. First, we extend NP-hardness of the weighted version in a certain class to the unweighted case. Second, we strengthen polynomial-time solvability of the unweighted version in the class of -free graphs to the weighted case. This result is tight in the sense that both versions are NP-hard in the class of -free graphs, i.e. is a minimal graph forbidding of which produces an NP-hard case for both versions of the problem

A Bayesian approach to constrained single- and multi-objective optimization

This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can be used to carry out the optimization is very limited, as in complex industrial design optimization problems. The method we propose to overcome this difficulty has its roots in both the Bayesian and the multi-objective optimization literatures. More specifically, an extended domination rule is used to handle objectives and constraints in a unified way, and a corresponding expected hyper-volume improvement sampling criterion is proposed. This new criterion is naturally adapted to the search of a feasible point when none is available, and reduces to existing Bayesian sampling criteria---the classical Expected Improvement (EI) criterion and some of its constrained/multi-objective extensions---as soon as at least one feasible point is available. The calculation and optimization of the criterion are performed using Sequential Monte Carlo techniques. In particular, an algorithm similar to the subset simulation method, which is well known in the field of structural reliability, is used to estimate the criterion. The method, which we call BMOO (for Bayesian Multi-Objective Optimization), is compared to state-of-the-art algorithms for single- and multi-objective constrained optimization

Riesz external field problems on the hypersphere and optimal point separation

We consider the minimal energy problem on the unit sphere $\mathbb{S}^d$ in the Euclidean space $\mathbb{R}^{d+1}$ in the presence of an external field $Q$, where the energy arises from the Riesz potential $1/r^s$ (where $r$ is the Euclidean distance and $s$ is the Riesz parameter) or the logarithmic potential $\log(1/r)$. Characterization theorems of Frostman-type for the associated extremal measure, previously obtained by the last two authors, are extended to the range $d-2 \leq s < d - 1.$ The proof uses a maximum principle for measures supported on $\mathbb{S}^d$. When $Q$ is the Riesz $s$-potential of a signed measure and $d-2 \leq s <d$, our results lead to explicit point-separation estimates for $(Q,s)$-Fekete points, which are $n$-point configurations minimizing the Riesz $s$-energy on $\mathbb{S}^d$ with external field $Q$. In the hyper-singular case $s > d$, the short-range pair-interaction enforces well-separation even in the presence of more general external fields. As a further application, we determine the extremal and signed equilibria when the external field is due to a negative point charge outside a positively charged isolated sphere. Moreover, we provide a rigorous analysis of the three point external field problem and numerical results for the four point problem.Comment: 35 pages, 4 figure

More Applications of the d-Neighbor Equivalence: Connectivity and Acyclicity Constraints

In this paper, we design a framework to obtain efficient algorithms for several problems with a global constraint (acyclicity or connectivity) such as Connected Dominating Set, Node Weighted Steiner Tree, Maximum Induced Tree, Longest Induced Path, and Feedback Vertex Set. For all these problems, we obtain 2^O(k)* n^O(1), 2^O(k log(k))* n^O(1), 2^O(k^2) * n^O(1) and n^O(k) time algorithms parameterized respectively by clique-width, Q-rank-width, rank-width and maximum induced matching width. Our approach simplifies and unifies the known algorithms for each of the parameters and match asymptotically also the running time of the best algorithms for basic NP-hard problems such as Vertex Cover and Dominating Set. Our framework is based on the d-neighbor equivalence defined in [Bui-Xuan, Telle and Vatshelle, TCS 2013]. The results we obtain highlight the importance and the generalizing power of this equivalence relation on width measures. We also prove that this equivalence relation could be useful for Max Cut: a W[1]-hard problem parameterized by clique-width. For this latter problem, we obtain n^O(k), n^O(k) and n^(2^O(k)) time algorithm parameterized by clique-width, Q-rank-width and rank-width

Girls, girls, girls: gender composition and female school choice

Gender segregation in the labor market may be explained by women's re- luctance to choose technical occupations, although the foundations for career choices are certainly laid earlier, during education. Educational experts claim that female students are doing better in math and science and are more likely to choose those subjects if they are in single-sex classes. Possible explanations are the lack of self-confidence of girls in male-dominated subjects, the domi- nating behavior of boys in the classroom and unequal treatment by teachers. In this paper, we identify the causal impact of gender composition in coedu- cational classes on the choice of school type for female students. We propose that girls are less likely to choose a female-dominated school type at the age of 14 after spending the previous years in classes with a higher share of female students. We address the problem of endogenous school choice by using nat- ural variation in gender composition of adjacent cohorts within schools. The results are clear-cut and survive powerful falsification and sensitivity checks: Females are less likely to choose a female-dominated school type and more likely to choose the technical school type if they were exposed to a higher share of girls in previous grades. Our paper contributes to the recent debate about coeducation either in certain subjects or at the school level.gender segregation, coeducation, career choice

TASI Lectures on Cosmological Perturbations

We present a self-contained summary of the theory of linear cosmological perturbations. We emphasize the effect of the six parameters of the minimal cosmological model, first, on the spectrum of Cosmic Microwave Background temperature anisotropies, and second, on the linear matter power spectrum. We briefly review at the end the possible impact of a few non-minimal dark matter and dark energy models.Comment: TASI 2013 lecture note

Stream Sampling for Frequency Cap Statistics

Unaggregated data, in streamed or distributed form, is prevalent and come from diverse application domains which include interactions of users with web services and IP traffic. Data elements have {\em keys} (cookies, users, queries) and elements with different keys interleave. Analytics on such data typically utilizes statistics stated in terms of the frequencies of keys. The two most common statistics are {\em distinct}, which is the number of active keys in a specified segment, and {\em sum}, which is the sum of the frequencies of keys in the segment. Both are special cases of {\em cap} statistics, defined as the sum of frequencies {\em capped} by a parameter $T$, which are popular in online advertising platforms. Aggregation by key, however, is costly, requiring state proportional to the number of distinct keys, and therefore we are interested in estimating these statistics or more generally, sampling the data, without aggregation. We present a sampling framework for unaggregated data that uses a single pass (for streams) or two passes (for distributed data) and state proportional to the desired sample size. Our design provides the first effective solution for general frequency cap statistics. Our $\ell$-capped samples provide estimates with tight statistical guarantees for cap statistics with $T=\Theta(\ell)$ and nonnegative unbiased estimates of {\em any} monotone non-decreasing frequency statistics. An added benefit of our unified design is facilitating {\em multi-objective samples}, which provide estimates with statistical guarantees for a specified set of different statistics, using a single, smaller sample.Comment: 21 pages, 4 figures, preliminary version will appear in KDD 201