1 The cover of plant species was recorded annually from 1988 to 2000 in nine spatially replicated plots in a species-rich, semi-natural meadow at Negrentino (southern Alps). This period showed large climatic variation and included the centennial maximum and minimum frequency of days with ≥ 10 mm of rain. 2 Changes in species composition were compared between three 4-year intervals characterized...

We study the problem of minimizing the expected loss of a linear predictor while constraining its sparsity, i.e., bounding the number of features used by the predictor. While the resulting optimization problem is generally NP-hard, several approximation algorithms are considered. We analyze the performance of these algorithms, focusing on the characterization of the trade-off between accuracy a...

Neural-based branch predictors have been among the most accurate in the literature. The recently proposed scaled neural analog predictor, or SNAP, builds on piecewise-linear branch prediction and relies on a mixed analog/digital implementation to mitigate latency as well as power requirements over previous neural predictors. I present an optimized version of the SNAP predictor, hybridized with ...

We establishe the polynomial convergence of a new class of pathfollowing methods for linear complementarity problems (LCP). Namely, we show that the predictor-corrector methods based on the L2 norm neighborhood. Mathematics Subject Classification: 90C33, 65G20, 65G50

In this paper, a new predictor-corrector method is proposed for solving sufficient linear complementarity problems (LCP) with an infeasible starting point. The method generates a sequence of iterates in a wide and symmetric neighborhood of the infeasible central path of the LCP. If the starting point is feasible or close to being feasible, then an ε-approximate solution is obtained in at most O...

In this paper, we study iteration complexities of Mizuno-Todd-Ye predictor-corrector (MTY-PC) algorithms in SDP and symmetric cone programs by way of curvature integrals. The curvature integral is defined along the central path, reflecting the geometric structure of the central path. The idea to exploit the curvature of the central path for the analysis of iteration complexities is based on the...

We propose primal-dual path-following Mehrotra-type predictor-corrector methods for solving convex quadratic semidefinite programming (QSDP) problems of the form: minX{2X • Q(X) + C • X : A(X) = b,X 0}, where Q is a self-adjoint positive semidefinite linear operator on Sn, b ∈ Rm, and A is a linear map from Sn to Rm. At each interior-point iteration, the search direction is computed from a dens...

Industry control processes presents many challenging problems, including non-linear or variable linear dynamic behaviour, variable time delay that means time varying parameters. One of the alternatives to handle with time delay systems is to use prediction technique to compensate the negative influence of the time delay. Smith predictor control (SPC) is one of the simplest and most often used s...

We propose a family of search directions based on primal-dual entropy in the contextof interior-point methods for linear optimization. We show that by using entropy based searchdirections in the predictor step of a predictor-corrector algorithm together with a homogeneousself-dual embedding, we can achieve the current best iteration complexity bound for linear opti-mization. The...

In this work a new optimized symmetric eight-step embedded predictor-corrector method (EPCM) with minimal phase-lag and algebraic order ten is presented. The method is based on the symmetric multistep method of Quinlan-Tremaine [1], with eight steps and eighth algebraic order and is constructed to solve numerically IVPs with oscillatory solutions. We compare the new method to some recently cons...