We present a target-following framework for semidefinite programming, which generalizes the target-following framework for linear programming. We use this framework to build weighted path-following interior-point algorithms of three distinct flavors: short-step, predictor-corrector, and large-update. These algorithms have worst-case iteration bounds that parallel their counterparts in linear pr...

The theory of self-scaled conic programming provides a uniied framework for the theories of linear programming, semideenite programming and convex quadratic programming with convex quadratic constraints. In the linear programming literature there exists a unifying framework for the analysis of various important classes of interior-point algorithms, known under the name of target-following algor...

In this work, we propose a predictor-corrector interior point method for linear programming in a primal-dual context, where the next iterate is chosen by the minimization of a polynomial merit function of three variables: the first is the steplength, the second defines the central path and the third models the weight of a corrector direction. The merit function minimization is performed by rest...

This paper proposes an Adaptive Smith Predictor Controller (ASPC) based on Neuro-Fuzzy Hammerstein Models (NFHM) with on-line non-linear model parameters identification. The NFHM approach uses a zeroorder Takagi-Sugeno fuzzy model to approximate the non-linear static function that is tuned off-line using gradient decent algorithm and to identify the linear dynamic function it is used the Recurs...

In this paper we present an infeasible start path following predictor corrector method for semideenite linear programming problem. This method does not assume that the dual pair of semideenite programs have feasible solutions, and, in at most O(jlog((A;b;C))jn) iterations of the predictor corrector method, nds either an approximate solution to the dual pair or shows that there is no optimal sol...

We extend the common linear functional regression model to the case where the dependency of a scalar response on a functional predictor is of polynomial rather than linear nature. Focusing on the quadratic case, we demonstrate the usefulness of the polynomial functional regression model which encompasses linear functional regression as a special case. Our approach works under mild conditions fo...

In this paper, we consider semiparametric varying coefficient partially linear models when the predictor variables of the linear part are ultra-high dimensional where the dimensionality grows exponentially with the sample size. We propose a profile forward regression (PFR) method to perform variable screening for ultra-high dimensional linear predictor variables. The proposed PFR algorithm can ...

We present a “twice universal” linear prediction algorithm over the unknown parameters and model orders, in which the sequentially accumulated square prediction error is as good as any linear predictor of order up to some M , for any individual sequence. The extra loss comprises of a parameter “redundancy” term proportional to (p/2)n-’ In(h), and a model order “redundancy” term proportional to ...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...