Search results for "Mathematical optimization"
showing 10 items of 1300 documents
A Comparison between Three Meta-Modeling Optimization Approaches to Design a Tube Hydroforming Process
2012
Computer aided procedures to design and optimize forming processes have become crucial research topics as the industrial interest in cost and time reduction has been increasing. A standalone numerical simulation approach could make the design too time consuming while meta-modeling techniques enables faster approximation of the investigated phenomena, reducing the simulation time. Many researchers are, nowadays, facing such research challenge by using various approaches. Response surface method (RSM) is probably the most known one, since its effectiveness was demonstrated in the past years. The effectiveness of RSM depends both on the definition of the Design of Experiments (DoE) and the acc…
Polynomial Fuzzy Models for Nonlinear Control: A Taylor Series Approach
2009
Classical Takagi-Sugeno (T-S) fuzzy models are formed by convex combinations of linear consequent local models. Such fuzzy models can be obtained from nonlinear first-principle equations by the well-known sector-nonlinearity modeling technique. This paper extends the sector-nonlinearity approach to the polynomial case. This way, generalized polynomial fuzzy models are obtained. The new class of models is polynomial, both in the membership functions and in the consequent models. Importantly, T-S models become a particular case of the proposed technique. Recent possibilities for stability analysis and controller synthesis are also discussed. A set of examples shows that polynomial modeling is…
Determination of thermometric parameters from the conductance curve of the normal metal based tunnel junction array
1997
Abstract We propose a method for extracting thermometric parameters from the measured conductance curve, against bias voltage, of a tunnel junction array. Instead of fitting the whole theoretical conductance curve to the experiment, we perform several polynomial fits to selected bias regions. The advantages of this method is that polynomial fits are linear in their fitting parameters whereas the theoretical form for the conductance is inherently nonlinear. This way the proposed method is about three orders of magnitude faster than the nonlinear fit. Optimizing this polynomial fit procedure is discussed.
Continuous-Variable Instantaneous Quantum Computing is Hard to Sample
2017
Instantaneous quantum computing is a sub-universal quantum complexity class, whose circuits have proven to be hard to simulate classically in the Discrete-Variable (DV) realm. We extend this proof to the Continuous-Variable (CV) domain by using squeezed states and homodyne detection, and by exploring the properties of post-selected circuits. In order to treat post-selection in CVs we consider finitely-resolved homodyne detectors, corresponding to a realistic scheme based on discrete probability distributions of the measurement outcomes. The unavoidable errors stemming from the use of finitely squeezed states are suppressed through a qubit-into-oscillator GKP encoding of quantum information,…
Benchmarking parameter-free AMaLGaM on functions with and without noise.
2013
We describe a parameter-free estimation-of-distribution algorithm (EDA) called the adapted maximum-likelihood Gaussian model iterated density-estimation evolutionary algorithm (AMaLGaM-ID[Formula: see text]A, or AMaLGaM for short) for numerical optimization. AMaLGaM is benchmarked within the 2009 black box optimization benchmarking (BBOB) framework and compared to a variant with incremental model building (iAMaLGaM). We study the implications of factorizing the covariance matrix in the Gaussian distribution, to use only a few or no covariances. Further, AMaLGaM and iAMaLGaM are also evaluated on the noisy BBOB problems and we assess how well multiple evaluations per solution can average ou…
Optimal Impulse Control Problems and Linear Programming
2009
Optimal impulse control problems are, in general, difficult to solve. A current research goal is to isolate those problems that lead to tractable solutions. In this paper, we identify a special class of optimal impulse control problems which are easy to solve. Easy to solve means that solution algorithms are polynomial in time and therefore suitable to the on-line implementation in real-time problems. We do this by using a paradigm borrowed from the Operations Research field. As main result, we present a solution algorithm that converges to the exact solution in polynomial time. Our approach consists in approximating the optimal impulse control problem via a binary linear programming proble…
The use of Markovian metapopulation models: a comparison of three methods reducing the dimensionality of transition matrices.
2001
The use of Markovian models is an established way for deriving the complete distribution of the size of a population and the probability of extinction. However, computationally impractical transition matrices frequently result if this mathematical approach is applied to natural populations. Binning, or aggregating population sizes, has been used to permit a reduction in the dimensionality of matrices. Here, we present three deterministic binning methods and study the errors due to binning for a metapopulation model. Our results indicate that estimation errors of the investigated methods are not consistent and one cannot make generalizations about the quality of a method. For some compared o…
New facets and an enhanced branch-and-cut for the min-max K -vehicles windy rural postman problem
2011
[EN] The min-max windy rural postman problem is a multiple vehicle version of the windy rural postman problem, WRPP, which consists of minimizing the length of the longest route to find a set of balanced routes for the vehicles. In a previous paper, an ILP formulation and a partial polyhedral study were presented, and a preliminary branch-and-cut algorithm that produced some promising computational results was implemented. In this article, we present further results for this problem. We describe several new facet-inducing inequalities obtained from the WRPP, as well as some inequalities that have to be satisfied by any optimal solution. We present an enhanced branch-and-cut algorithm that t…
A Perturbation Approach to Continuous-Time Portfolio Selection Under Stochastic Investment Opportunities
2013
This paper studies portfolio selection in continuous-time models with stochastic investment opportunities. We consider asset allocation problems where preferences are specified as power utility derived from terminal wealth as well as consumption-savings problems with recursive utility Epstein-Zin preferences. The paper approximates the associated dynamic programming problem by perturbing the coefficients of the stochastic dynamics. We represent the Hamilton-Jacobi-Bellman equation as a series of partial differential equations that can be solved iteratively in closed-form through computer algebra software, at any desired accuracy.
Coherence Checking and Propagation of Lower Probability Bounds
2003
In this paper we use imprecise probabilities, based on a concept of generalized coherence (g-coherence), for the management of uncertain knowledge and vague information. We face the problem of reducing the computational difficulties in g-coherence checking and propagation of lower conditional probability bounds. We examine a procedure, based on linear systems with a reduced number of unknowns, for the checking of g-coherence. We propose an iterative algorithm to determine the reduced linear systems. Based on the same ideas, we give an algorithm for the propagation of lower probability bounds. We also give some theoretical results that allow, by suitably modifying our algorithms, the g-coher…