Flow network based generative models for non-iterative diverse candidate generation
This paper is about the problem of learning a stochastic policy for generating an object (like
a molecular graph) from a sequence of actions, such that the probability of generating an …
a molecular graph) from a sequence of actions, such that the probability of generating an …
A gradient-based neural network method for solving strictly convex quadratic programming problems
A Nazemi, M Nazemi - Cognitive computation, 2014 - Springer
In this paper, we study a gradient-based neural network method for solving strictly convex
quadratic programming (SCQP) problems. By converting the SCQP problem into a system of …
quadratic programming (SCQP) problems. By converting the SCQP problem into a system of …
Fractional power series neural network for solving delay fractional optimal control problems
F Kheyrinataj, A Nazemi - Connection Science, 2020 - Taylor & Francis
In this paper, we develop a numerical method for solving the delay optimal control problems
of fractional-order. The fractional derivatives are considered in the Caputo sense. The …
of fractional-order. The fractional derivatives are considered in the Caputo sense. The …
A capable neural network framework for solving degenerate quadratic optimization problems with an application in image fusion
A Nazemi - Neural Processing Letters, 2018 - Springer
This paper presents a dynamic optimization scheme for solving degenerate convex
quadratic programming (DCQP) problems. According to the saddle point theorem …
quadratic programming (DCQP) problems. According to the saddle point theorem …
On portfolio management with value at risk and uncertain returns via an artificial neural network scheme
S Mohammadi, A Nazemi - Cognitive Systems Research, 2020 - Elsevier
This paper focuses on the computation issue of portfolio optimization with scenario-based
Value-at-Risk. The main idea is to replace the portfolio selection models with linear …
Value-at-Risk. The main idea is to replace the portfolio selection models with linear …
Solving general convex nonlinear optimization problems by an efficient neurodynamic model
A Nazemi - Engineering Applications of Artificial Intelligence, 2013 - Elsevier
In this paper, a neural network model is constructed on the basis of the duality theory,
optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle …
optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle …
Nature-inspired computational intelligence integration with Nelder–Mead method to solve nonlinear benchmark models
In the present study, nature-inspired computing technique has been designed for the
solution of nonlinear systems by exploiting the strength of particle swarm optimization (PSO) …
solution of nonlinear systems by exploiting the strength of particle swarm optimization (PSO) …
An application of a merit function for solving convex programming problems
This paper presents a gradient neural network model for solving convex nonlinear
programming (CNP) problems. The main idea is to convert the CNP problem into an …
programming (CNP) problems. The main idea is to convert the CNP problem into an …
Nonlinear fractional optimal control problems with neural network and dynamic optimization schemes
S Ghasemi, A Nazemi, S Hosseinpour - Nonlinear Dynamics, 2017 - Springer
This paper deals with a numerical technique for fractional optimal control problems (FOCPs)
based on a neural network scheme. The fractional derivative in these problems is in the …
based on a neural network scheme. The fractional derivative in these problems is in the …
[HTML][HTML] An efficient dynamic model for solving a portfolio selection with uncertain chance constraint models
This paper presents a neural network model for solving maximization programming model
with chance constraint, in which the security returns are uncertain variables. The main idea …
with chance constraint, in which the security returns are uncertain variables. The main idea …