This work deals with semantics of programming languages (or equivalently, thanks to Curry-Howard isomorphism, with semantics of proofs). We introduce a framework in which both static semantics (Ehrhard’s hypercoherences) and dynamic semantics (Hyland-Ong’s games), can be presented. The work is carried in a multiplicative subsystem of Laurent’s polarized linear logic with weakening. Like Böhm tr...

This paper describes the synthesis of non-fragile or resilient regulators for linear systems. A general framework for fragility is described using state-space methodologies, and the LQ/H 2 static state-feedback problem is examined in detail. We discuss the multiplicative structured uncertainties case, and propose remedies of the fragility problem using an optimization programming framework via ...

We discuss an application of the well-known Multiplicative Weights Update (MWU) algorithm to non-convex and mixed-integer nonlinear programming. We present applications to: (a) the distance geometry problem, which arises in the positioning of mobile sensors and in protein conformation; (b) a hydro unit commitment problem arising in the energy industry, and (c) a class of Markowitz’ portfolio se...

We introduce a new framework for solving distributed constraint optimization problems that extend the domain of each variable into a simplex. We propose two methods for searching the extended domain for good assignments. The first one relaxes the problem using linear programming, finds the optimum LP solution, and rounds it to an assignment. The second one plays a cost-minimization game, finds ...

We considers how a particular kind of graph corresponds to multiplicative intuitionistic linear logic formula. The main feature of the graphical notation is that it absorbs certain symmetries between conjunction and implication. We look at the basic definitions and present details of an implementation in the functional programming language Standard ML. This provides a functional approach to gra...

New feature selection algorithms for linear threshold functions are described which combine backward elimination with an adaptive regularization method. This makes them particularly suitable to the classification of microarray expression data, where the goal is to obtain accurate rules depending on few genes only. Our algorithms are fast and easy to implement, since they center on an incrementa...

A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the ...

This paper discusses the instability of eleven nonlinear state space models that underly exponential smoothing. Hyndman et al. (2002) proposed a framework of 24 state space models for exponential smoothing, including the well-known simple exponential smoothing, Holt’s linear and Holt-Winters’ additive and multiplicative methods. This was extended to 30 models with Taylor’s (2003) damped multipl...

Sell’s approach [32] to the construction of attractors for the Navier– Stokes equations in 3-dimensions is extended to the 3-D stochastic equations with a general multiplicative noise.

In this paper, the linear quadratic regulation problem for discrete-time systems with state delays and multiplicative noise is considered. The necessary and sufficient condition for the problem admitting a unique solution is given. Under this condition, the optimal feedback control and the optimal cost are presented via a set of coupled difference equations. Our approach is based on the maximum...