This paper presents the identification of the impact force acting on a simply supported beam. The force identification is an inverse problem in which the measured response of the structure is used to determine the applied force. The identification problem is formulated as an optimization problem and the genetic algorithm is utilized to solve the optimization problem. The objective function is c...

In this paper we provide an algorithm allowing to solve the variational Bayesian issue as a functional optimization problem. The main contribution of this paper is to transpose a classical iterative algorithm of optimization in the metric space of probability densities involved in the Bayesian methodology. The main advantage of this methodology is that it allows to address large dimensional inv...

We consider the integer programming version of inverse optimization. Using superadditive duality, we provide a polyhedral description of the set of inversefeasible objectives. We then describe two algorithmic approaches for solving the inverse integer programming problem.

In this paper, we address the inverse problem, or the statistical machine learning problem, in Markov random fields with a non-parametric pair-wise energy function with continuous variables. The inverse problem is formulated by maximum likelihood estimation. The exact treatment of maximum likelihood estimation is intractable because of two problems: (1) it includes the evaluation of the partiti...

In this paper, we propose a novel approach to dynamic inverse optimization by the learning of neural networks. A dynamic inverse optimization problem here means to estimate a criterion function under which given input and output sequences become optimal for a known state equation model. A neural network architecture representing the optimality condition including an algebraic Riccati equation i...

How optimal are human movements ? This thesis tackles this issue by focusing especially on climbing movements, studied here under three complementary aspects which are the experimental gathering of climbing sequences, the biomechanical analysis of these data, and the synthesis of gestures by timing optimization. Walking has been largely studied, with good results in animation [Mordatch 2013]. W...

Linear programming duality yields e,cient algorithms for solving inverse linear programs. We show that special classes of conic programs admit a similar duality and, as a consequence, establish that the corresponding inverse programs are e,ciently solvable. We discuss applications of inverse conic programming in portfolio optimization and utility function identi0cation. c © 2004 Elsevier B.V. A...

In this paper we provide an algorithm allowing to solve the variational Bayesian issue as a functional optimization problem. The main contribution of this paper is to transpose a classical iterative algorithm of optimization in the metric space of measures involved in the Bayesian methodology. Once given the convergence properties of this algorithm, we consider its application to large dimensio...

In this paper, we consider the partial inverse assignment problem under l1 norm without bound constraints. We show that the partial inverse problem can be solved by a strongly polynomial algorithm. The technique for solving this problem can be extended to handle a special type of partial inverse 0.1 combinatorial optimization problems. © 2006 Elsevier B.V. All rights reserved.

in this study, hydrus2d/3d software was used to estimate the hydraulic parameters of van genuchten-mualem model via inverse modeling using double-ring infiltrometers data in 3 different soil textures. nine scenarios of inverse modeling (divided in three groups) were considered with different number (5, 4 and 3) of fitted hydraulic parameters for optimization. in the first group, simulation was ...