the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

where σ T (x) = κ(x,x) − k1:T (x)K−1k1:T (x) and this bound is tight. Moreover, σ T (x) is the posterior predictive variance of a Gaussian process with the same kernel. Lemma 3 (Adapted from Proposition 1 of de Freitas et al. (2012)). Let κ : R × R → R be a kernel that is twice differentiable along the diagonal {(x,x) |x ∈ RD}, with L defined as in Lemma 1.1, and f be an element of the RKHS wit...

Clustering is a fundamental task in unsupervised learning. The focus of this paper is the Correlation Clustering functional which combines positive and negative affinities between the data points. The contribution of this paper is two fold: (i) Provide a theoretic analysis of the functional. (ii) New optimization algorithms which can cope with large scale problems (> 100K variables) that are in...

Nowadays, multimedia streaming is a common service on the Internet including both fixed and mobile networks. The RTP protocol is usually used to transfer the media. The RTCP protocol is an accompanying protocol for the RTP and it is used for feedback transmission in order to control multimedia session behaviour. The issue is the bandwidth dedicated for the RTCP protocol. As defined in the RTP s...

This paper proposes a new multi-scale energy minimization algorithm which can be used to efficiently solve large scale labelling problems in computer vision. The basic modus operandi of any multi-scale method involves the construction of a smaller problem which can be solved efficiently. The solution of this problem is used to obtain a partial labelling of the original energy function, which in...

This article proposes a methodology for developing optimal transit networks (route structures and headways) that minimizes transit transfers and total user cost while maximizing service coverage, given information on transit demand, transit fleet size, and the street network of the transit service area. The research provides an effective mathematical computational tool with minimal reliance on ...

Optimization problems involving eigenvalues arise in many applications. Let x be a vector of real parameters and let A(x) be a continuously diierentiable symmetric matrix function of x. We consider a particular problem which occurs frequently: the minimization of the maximum eigenvalue of A(x), subject to linear constraints and bounds on x. The eigenvalues of A(x) are not diierentiable at point...

An algorithm for solving large-scale nonlinear' programs with linear constraints is presented. The method combines efficient sparse-matrix techniques as in the revised simplex method with stable quasi-Newton methods for handling the nonlinearities. A general-purpose production code (MINOS) is described, along with computational experience on a wide variety of problems.