134 research outputs found
Fast Label Embeddings via Randomized Linear Algebra
Many modern multiclass and multilabel problems are characterized by
increasingly large output spaces. For these problems, label embeddings have
been shown to be a useful primitive that can improve computational and
statistical efficiency. In this work we utilize a correspondence between rank
constrained estimation and low dimensional label embeddings that uncovers a
fast label embedding algorithm which works in both the multiclass and
multilabel settings. The result is a randomized algorithm whose running time is
exponentially faster than naive algorithms. We demonstrate our techniques on
two large-scale public datasets, from the Large Scale Hierarchical Text
Challenge and the Open Directory Project, where we obtain state of the art
results.Comment: To appear in the proceedings of the ECML/PKDD 2015 conference.
Reference implementation available at https://github.com/pmineiro/randembe
Centerpoints: a link between optimization and convex geometry
We introduce a concept that generalizes several different notions of a âcenterpointâ in the literature. We develop an oracle-based algorithm for convex mixed-integer optimization based on centerpoints. Further, we show that algorithms based on centerpoints are âbest possibleâ in a certain sense. Motivated by this, we establish several structural results about this concept and provide efficient algorithms for computing these points
Truss geometry and topology optimization with global stability constraints
In this paper, we introduce geometry optimization into an existing topology optimization workflow for truss structures with global stability constraints, assuming a linear buckling analysis. The design variables are the cross-sectional areas of the bars and the coordinates of the joints. This makes the optimization problem formulations highly nonlinear and yields nonconvex semidefinite programming problems, for which there are limited available numerical solvers compared with other classes of optimization problems. We present problem instances of truss geometry and topology optimization with global stability constraints solved using a standard primal-dual interior point implementation. During the solution process, both the cross-sectional areas of the bars and the coordinates of the joints are concurrently optimized. Additionally, we apply adaptive optimization techniques to allow the joints to navigate larger move limits and to improve the quality of the optimal designs
Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV
The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of âs = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pTâ„20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60â€pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2â€{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration
Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector
The inclusive and dijet production cross-sections have been measured for jets
containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass
energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The
measurements use data corresponding to an integrated luminosity of 34 pb^-1.
The b-jets are identified using either a lifetime-based method, where secondary
decay vertices of b-hadrons in jets are reconstructed using information from
the tracking detectors, or a muon-based method where the presence of a muon is
used to identify semileptonic decays of b-hadrons inside jets. The inclusive
b-jet cross-section is measured as a function of transverse momentum in the
range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet
cross-section is measured as a function of the dijet invariant mass in the
range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets
and the angular variable chi in two dijet mass regions. The results are
compared with next-to-leading-order QCD predictions. Good agreement is observed
between the measured cross-sections and the predictions obtained using POWHEG +
Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet
cross-section. However, it does not reproduce the measured inclusive
cross-section well, particularly for central b-jets with large transverse
momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final
version published in European Physical Journal
A simple randomised algorithm for convex optimisation: Application to two-stage stochastic programming
We consider maximising a concave function over a convex set by a simple randomised algorithm. The strength of the algorithm is that it requires only approximate function evaluations for the concave function and a weak membership oracle for the convex set. Under smoothness conditions on the function and the feasible set, we show that our algorithm computes a near-optimal point in a number of operations which is bounded by a polynomial function of all relevant input parameters and the reciprocal of the desired precision, with high probability. As an application to which the features of our algorithm are particularly useful we study two-stage stochastic programming problems. These problems have the property that evaluation of the objective function is #P-hard under appropriate assumptions on the models. Therefore, as a tool within our randomised algorithm, we devise a fully polynomial randomised approximation scheme for these function evaluations, under appropriate assumptions on the models. Moreover, we deal with smoothing the feasible set, which in two-stage stochastic programming is a polyhedron
Topological insights in polynuclear Ni/Na coordination clusters derived from a schiff base ligand
This article presents the syntheses, crystal structures, topological features and magnetic properties of two NiII/NaI coordination clusters (CCs) formulated [NiII3Na(L1)3(HL1 (MeOH)2] (1) and [NiII6Na(L1)5(CO3)(MeO (MeOH)3(H2O)3]·4(MeOH) 2(H2O) [2 4(MeOH) 2(H2O)] where H2L1 is the semi rigid Schiff base ligand (E)-2-(2-hydroxy-3 methoxybenzylideneamino)-phenol). Compound 1 possesses a rare NiII3NaI cubane (3M4-1) topology and compound 2 is the first example in polynuclear Ni/Na chemistry that exhibits a 2,3,4M7-1 topology
Matrix-free interior point method for compressed sensing problems
We consider a class of optimization problems for sparse signal reconstruction
which arise in the field of Compressed Sensing (CS). A plethora of approaches
and solvers exist for such problems, for example GPSR, FPC AS, SPGL1, NestA,
\ell_{1}_\ell_{s}, PDCO to mention a few. Compressed Sensing applications
lead to very well conditioned optimization problems and therefore can be solved
easily by simple first-order methods. Interior point methods (IPMs) rely on the
Newton method hence they use the second-order information. They have numerous
advantageous features and one clear drawback: being the second-order approach
they need to solve linear equations and this operation has (in the general
dense case) an computational complexity. Attempts have been made to
specialize IPMs to sparse reconstruction problems and they have led to
interesting developments implemented in and PDCO softwares. We
go a few steps further. First, we use the matrix-free interior point method, an
approach which redesigns IPM to avoid the need to explicitly formulate (and
store) the Newton equation systems. Secondly, we exploit the special features
of the signal processing matrices within the matrix-free IPM. Two such features
are of particular interest: an excellent conditioning of these matrices and the
ability to perform inexpensive (low complexity) matrix-vector multiplications
with them. Computational experience with large scale one-dimensional signals
confirms that the new approach is efficient and offers an attractive
alternative to other state-of-the-art solvers
Large-scale unit commitment under uncertainty: an updated literature survey
The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject
A model species for agricultural pest genomics: the genome of the Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera: Chrysomelidae)
The Colorado potato beetle is one of the most challenging agricultural pests to manage. It has shown a spectacular ability to adapt to a variety of solanaceaeous plants and variable climates during its global invasion, and, notably, to rapidly evolve insecticide resistance. To examine evidence of rapid evolutionary change, and to understand the genetic basis of herbivory and insecticide resistance, we tested for structural and functional genomic changes relative to other arthropod species using genome sequencing, transcriptomics, and community annotation. Two factors that might facilitate rapid evolutionary change include transposable elements, which comprise at least 17% of the genome and are rapidly evolving compared to other Coleoptera, and high levels of nucleotide diversity in rapidly growing pest populations. Adaptations to plant feeding are evident in gene expansions and differential expression of digestive enzymes in gut tissues, as well as expansions of gustatory receptors for bitter tasting. Surprisingly, the suite of genes involved in insecticide resistance is similar to other beetles. Finally, duplications in the RNAi pathway might explain why Leptinotarsa decemlineata has high sensitivity to dsRNA. The L. decemlineata genome provides opportunities to investigate a broad range of phenotypes and to develop sustainable methods to control this widely successful pest
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