Implementing numerical optimization techniques (Gradient Descent, Conjugate Gradient, Nelder-Mead, BFGS) in R for (1.) univariate optimization, (2.) multivariate optimization, (3.) visualizing ...

Keywords: multivariate polynomial, numerical optimization, tensor decomposition, Gauss-Newton algorithm, factorization machines, higher order factorization machines, tensor network, image ...

Increasing the final titer of a multi-gene metabolic pathway can be viewed as a multivariate optimization problem. While numerous multivariate optimization algorithms exist, few are specifically ...

This paper presents a multivariate optimization technique for the numerical simulation of continuous dynamical systems whose parameters, functional forms and/or initial conditions are modelled by ...

Article citations More>> Varshney, R., Najmussehar and Ahsan, M.J. (2012) An Optimum Multivariate Stratified Double Sampling Design in Presence of Non-Response. Optimization Letters, 6, 993-1008. has ...

Multivariate polynomial optimization problems are ubiquitous in signal processing, machine learning, and artificial intelligence. Examples include, but are not limited to, blind source separation, ...

Since these maps are submodular (or supermodular) under some assumptions 1, we design greedy-based optimization algorithms with theoretical guarantees. We also perform numerical experiments on ...

Discover the power of Pareto-based multi-objective estimation of distribution algorithms with multivariate T-copulas. Improve optimization with probability distribution models and achieve better ...

With the advent of massively parallel computing coprocessors, numerical optimization for deep-learning disciplines is now possible. Complex real-time pattern recognition, for example, that can be used ...

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