Quality Of Approximation Research Articles

The Power of the Weighted Sum Scalarization for Approximating Multiobjective Optimization Problems

We determine the power of the weighted sum scalarization with respect to the computation of approximations for general multiobjective minimization and maximization problems. Additionally, we introduce a new multi-factor notion of approximation that is specifically tailored to the multiobjective case and its inherent trade-offs between different objectives. For minimization problems, we provide an efficient algorithm that computes an approximation of a multiobjective problem by using an exact or approximate algorithm for its weighted sum scalarization. In case that an exact algorithm for the weighted sum scalarization is used, this algorithm comes arbitrarily close to the best approximation quality that is obtainable by supported solutions – both with respect to the common notion of approximation and with respect to the new multi-factor notion. Moreover, the algorithm yields the currently best approximation results for several well-known multiobjective minimization problems. For maximization problems, however, we show that a polynomial approximation guarantee can, in general, not be obtained in more than one of the objective functions simultaneously by supported solutions.

Strategies to Enhance the Performance of Gaussian Mixture Model Fitting for Uncertainty Quantification

Summary When formulating history matching within the Bayesian framework, we may quantify the uncertainty of model parameters and production forecasts using conditional realizations sampled from the posterior probability density function (PDF). It is quite challenging to sample such a posterior PDF. Some methods [e.g., Markov chain Monte Carlo (MCMC)] are very expensive, whereas other methods are cheaper but may generate biased samples. In this paper, we propose an unconstrained Gaussian mixture model (GMM) fitting method to approximate the posterior PDF and investigate new strategies to further enhance its performance. To reduce the central processing unit (CPU) time of handling bound constraints, we reformulate the GMM fitting formulation such that an unconstrained optimization algorithm can be applied to find the optimal solution of unknown GMM parameters. To obtain a sufficiently accurate GMM approximation with the lowest number of Gaussian components, we generate random initial guesses, remove components with very small or very large mixture weights after each GMM fitting iteration, and prevent their reappearance using a dedicated filter. To prevent overfitting, we add a new Gaussian component only if the quality of the GMM approximation on a (large) set of blind-test data sufficiently improves. The unconstrained GMM fitting method with the new strategies proposed in this paper is validated using nonlinear toy problems and then applied to a synthetic history-matching example. It can construct a GMM approximation of the posterior PDF that is comparable to the MCMC method, and it is significantly more efficient than the constrained GMM fitting formulation (e.g., reducing the CPU time by a factor of 800 to 7,300 for problems we tested), which makes it quite attractive for large-scale history-matching problems. NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Compression and Visualization Interactive of 3D Mesh

The combination of compression and visualization is mentioned as perspective, very few articles treat with this problem. Indeed, in this paper, we proposed a new approach to multiresolution visualization based on a combination of segmentation and multiresolution mesh compression. For this, we proposed a new segmentation method that benefits the organization of faces of the mesh followed by a progressive local compression of regions of mesh to ensure the refinement local of the three-dimensional object. Thus, the quantization precision is adapted to each vertex during the encoding /decoding process to optimize the rate-distortion compromise. The optimization of the treated mesh geometry improves the approximation quality and the compression ratio at each level of resolution. The experimental results show that the proposed algorithm gives competitive results compared to the previous works dealing with the rate-distortion compromise and very satisfactory visual results.

Signal processing of nonlinear dynamic systems

The paper considers Hermite polynomials that act as a self-similar basis for the decomposition of functions in phase space. It is shown that the equations of behavior of nonlinear dynamical systems are simplified. It is also noted that the wavelet decomposition over Hermite polynomials reduces the number of approximation coefficients and improves the quality of approximation.

Application of the digital model thermal errors of machine tools in automated production

The article presents a predicting method for a machine tool thermal error based on a nonlinear autoregressive neural network with an external input, as well as methods for smoothing experimental data obtained from measuring devices by approximation using polynomial regression and the gray systems theory. The development of accurate and robust thermal models is a critical step in achieving high productivity in thermal deformation reduction techniques on machine tools. Because thermal deformations of the machine structure caused by temperature increase often lead to thermal errors and reduce the accuracy of machining parts. The use of neural networks is a promising direction in solving forecasting problems. The authors propose a block diagram of a thermal process digital twin based on a neural network, which can be used in automated production. The results of the experiment carried out for the machine model 400V are obtained in the form of an assessment of approximation quality and accuracy of the forecasting model. The results show that the use of the proposed smoothing methods and a model for predicting a machine tool thermal error based on a neural network can improve the forecast accuracy.

Two-stage instrumental variable estimation of linear panel data models with interactive effects

Summary This paper analyses the instrumental variables (IV) approach put forward by Norkute et al. (2021), in the context of static linear panel data models with interactive effects present in the error term and the regressors. Instruments are obtained from transformed regressors, thereby it is not necessary to search for external instruments. We consider a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis reveals that: (i) the $\sqrt{\textit {NT}}$-consistent 2SIV estimator is free from asymptotic bias that may arise due to the estimation error of the interactive effects, while (ii) existing estimators can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as existing estimators that eliminate interactive effects jointly in the regressors and the error, while (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results.

Reconstruction of a Symbolic Periodic Sequence from a Sequence with Noise

The problem of constructing a periodic sequence consisting of at least eight periods is considered, based on a given sequence obtained from an unknown periodic sequence, also containing at least eight periods, by introducing noise of deletion, replacement, and insertion of symbols. To construct a periodic sequence that approximates a given one, distorted by noise, it is first required to estimate the length of the repeating fragment (period). Further, the distorted original sequence is divided into successive sections of equal length; the length takes on integer values from 80 to 120 % of the period estimate. Each obtained section is compared with each of the remaining sections, a section is selected to build a periodic sequence that has the minimum edit distance (Levenshtein distance) to any of the remaining sections, minimization is carried out over all sections of a fixed length, and then along all lengths from 80 to 120 % of period estimates. For correct comparison of fragments of different lengths, we consider the ration between the edit distance and the length of the fragment. The length of a fragment that minimizes the ratio of the edit distance to another fragment of the same length to the fragment length is considered the period of the approximating periodic sequence, and the fragment itself, repeating the required number of times, forms an approximating sequence. The constructed sequence may contain an incomplete repeating fragment at the end. The quality of the approximation is estimated by the ratio of the edit distance from the original distorted sequence to the constructed periodic sequence of the same length and this length.

Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

Herein, an artificial neural network (ANN)-based approach for the efficient automated modeling and simulation of isotropic hyperelastic solids is presented. Starting from a large data set comprising deformations and corresponding stresses, a simple, physically based reduction of the problem’s dimensionality is performed in a data processing step. More specifically, three deformation type invariants serve as the input instead of the deformation tensor itself. In the same way, three corresponding stress coefficients replace the stress tensor in the output layer. These initially unknown values are calculated from a linear least square optimization problem for each data tuple. Using the reduced data set, an ANN-based constitutive model is trained by using standard machine learning methods. Furthermore, in order to ensure thermodynamic consistency, the previously trained network is modified by constructing a pseudo-potential within an integration step and a subsequent derivation which leads to a further ANN-based model. In the second part of this work, the proposed method is exemplarily used for the description of a highly nonlinear Ogden type material. Thereby, the necessary data set is collected from virtual experiments of discs with holes in pure plane stress modes, where influences of different loading types and specimen geometries on the resulting data sets are investigated. Afterwards, the collected data are used for the ANN training within the reduced data space, whereby an excellent approximation quality could be achieved with only one hidden layer comprising a low number of neurons. Finally, the application of the trained constitutive ANN for the simulation of two three-dimensional samples is shown. Thereby, a rather high accuracy could be achieved, although the occurring stresses are fully three-dimensional whereas the training data are taken from pure two-dimensional plane stress states.

AN ADAPTIVE METHOD USING THE LWENO RECONSTRUCTION

In this work, an adaptive formulation of the Legendre - Weighted Essentially NonOscillatory (L-WENO) method is used to solve some problems of two-dimensional linear conservation laws on unstructured triangular mesh. The mesh adaptivity is used to improve the performance of the method. Although the results with the L-WENO method gets better as the mesh is refined, the mesh adaptation algorithm was able to improve the quality of the numerical approximation and reduce computational cost by refining and coarsening the computational mesh based on some specified criteria.

Bounds in Tree-Based Approaches to Generate Project Portfolios in the Presence of Interactions

Portfolio decision models have become an important branch of decision analysis. Portfolio problems are inherently complex, because of the combinatorial explosion in the number of portfolios that can be constructed even from a small number of items. To efficiently construct a set of portfolios that provide good performance in multiple criteria, methods that guide the search process are needed. Such methods require the calculation of bounds to estimate the performance of portfolios that can be obtained from a given partial portfolio. The calculation of such bounds is particularly difficult if interactions between items in the portfolio are possible. In the paper, the authors introduce a method to represent such interactions and develop various bounds that can be used in the presence of interactions. These methods are then tested in a computational study, where they show that the bounds they propose frequently provide a good approximation of actual outcomes, and also analyze specific properties of the problem that influence the approximation quality of the proposed bounds.

Second-order fast–slow dynamics of non-ergodic Hamiltonian systems: Thermodynamic interpretation and simulation

A class of fast–slow Hamiltonian systems with potential Uɛ describing the interaction of non-ergodic fast and slow degrees of freedom is studied. The parameter ɛ indicates the typical timescale ratio of the fast and slow degrees of freedom. It is known that the Hamiltonian system converges for ɛ→0 to a homogenised Hamiltonian system. We study the situation where ɛ is small but positive.First, we rigorously derive the second-order corrections to the homogenised (slow) degrees of freedom. They can be decomposed into explicitly given terms that oscillate rapidly around zero and terms that trace the average motion of the corrections, which are given as the solution to an inhomogeneous linear system of differential equations.Then, we analyse the energy of the fast degrees of freedom expanded to second-order from a thermodynamic point of view. In particular, we define and expand to second-order a temperature, an entropy and external forces and show that they satisfy to leading-order, as well as on average to second-order, thermodynamic energy relations akin to the first and second law of thermodynamics.Finally, we analyse for a specific fast–slow Hamiltonian system the second-order asymptotic expansion of the slow degrees of freedom from a numerical point of view. Their approximation quality for short and long time frames and their total computation time are compared with those of the solution to the original fast–slow Hamiltonian system of similar accuracy.

Approximations for cumulative distribution function of standard normal

In this paper, Tocher’s approximation for standard normal distribution function is improved and three new approximations are proposed. The quality of the new approximations was computed based on two criteria; the maximum absolute error and the mean absolute error. We found that the maximum absolute errors of the proposed approximations fall between 7.62 × 10−7 and 4.95 × 10−5 and the mean absolute errors -based on 5001 values between 0 and 5- fall between 1.82 × 10−7 and 1.59 × 10−5.

ПАРАМЕТРИ НЕВ’ЯЗКИ АПРОКСИМАЦІЙ ДИСКРЕТНО ЗАДАНИХ ЗАЛЕЖНОСТЕЙ АНАЛІТИЧНИМИ ФУНКЦІЯМИ ТА КРИТЕРІЇ ПОШУКУ ОПТИМАЛЬНИХ ЗНАЧЕНЬ ЇХНІХ КОЕФІЦІЄНТІВ

Universal discrepancy parameters of approximations of discretely specified dependencies by analytical functions and search criteria for optimal values of their coefficients, as well as analysis of features of their application are described. Discrepancy parameters of approximations, which do not depend on the ranges of variation of the values of functions and the number of points of a discretely specified dependence, are proposed. They can be effective for objectively comparing the quality of approximations of any dependencies by any functions. Approximations of a discretely specified dependence of the mathematical expectation of the equivalent electrical resistance of a layer of aluminum granules during spark-erosion dispersion in water on the instantaneous values of the discharge current are carried out. As approximating functions, we chose a power function with an exponent factor –1 and a function based on exponential. Using the criteria of the least approximation error, the optimal values of the coefficients of both approximating functions are founded. It is shown in which cases it is advisable to use the combined search criteria for the optimal values of the coefficients of the approximating functions, and in which are enough simple one-component ones. Ref. 27, fig. 2, tables 2.

Performance Approximation for Time-Dependent Queues with Generally Distributed Abandonments

Many stochastic systems face a time-dependent demand. Especially in stochastic service systems, for example, in call centers, customers may leave the queue if their waiting time exceeds their personal patience. As discussed in the extant literature, it can be useful to use general distributions to model such customer patience. This paper analyzes the time-dependent performance of a multiserver queue with a nonhomogeneous Poisson arrival process with a time-dependent arrival rate, exponentially distributed processing times, and generally distributed time to abandon. Fast and accurate performance approximations are essential for decision support in such queueing systems, but the extant literature lacks appropriate methods for the setting we consider. To approximate time-dependent performance measures for small- and medium-sized systems, we develop a new stationary backlog-carryover (SBC) approach that allows for the analysis of underloaded and overloaded systems. Abandonments are considered in two steps of the algorithm: (i) in the approximation of the utilization as a reduced arrival stream and (ii) in the approximation of waiting-based performance measures with a stationary model for general abandonments. To improve the approximation quality, we discuss an adjustment to the interval lengths. We present a limit result that indicates convergence of the method for stationary parameters. The numerical study compares the approximation quality of different adjustments to the interval length. The new SBC approach is effective for instances with small numbers of time-dependent servers and gamma-distributed abandonment times with different coefficients of variation and for an empirical distribution of the abandonment times from real-world data obtained from a call center. A discrete-event simulation benchmark confirms that the SBC algorithm approximates the performance of the queueing system with abandonments very well for different parameter configurations. Summary of Contribution: The paper presents a fast and accurate numerical method to approximate the performance measures of a time‐dependent queueing system with generally distributed abandonments. The presented stationary backlog carryover approach with abandonment combines algorithmic ideas with stationary queueing models for generally distributed abandonment times. The reliability of the method is analyzed for transient systems and numerically studied with real‐world data.

Pareto optimization for subset selection with dynamic cost constraints

We consider the subset selection problem for function f with constraint bound B that changes over time. Within the area of submodular optimization, various greedy approaches are commonly used. For dynamic environments we observe that the adaptive variants of these greedy approaches are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a ϕ=(αf/2)(1−1eαf)-approximation, where αf is the submodularity ratio of f, for each possible constraint bound b≤B. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that B increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms. We also consider EAMC, a new evolutionary algorithm with polynomial expected time guarantee to maintain ϕ approximation ratio, and NSGA-II with two different population sizes as advanced multi-objective optimization algorithm, to demonstrate their challenges in optimizing the maximum coverage problem. Our empirical analysis shows that, within the same number of evaluations, POMC is able to perform as good as NSGA-II under linear constraint, while EAMC performs significantly worse than all considered algorithms in most cases.

On the Statistical Discrepancy and Affinity of Priority Vector Heuristics in Pairwise-Comparison-Based Methods.

There are numerous priority deriving methods (PDMs) for pairwise-comparison-based (PCB) problems. They are often examined within the Analytic Hierarchy Process (AHP), which applies the Principal Right Eigenvalue Method (PREV) in the process of prioritizing alternatives. It is known that when decision makers (DMs) are consistent with their preferences when making evaluations concerning various decision options, all available PDMs result in the same priority vector (PV). However, when the evaluations of DMs are inconsistent and their preferences concerning alternative solutions to a particular problem are not transitive (cardinally), the outcomes are often different. This research study examines selected PDMs in relation to their ranking credibility, which is assessed by relevant statistical measures. These measures determine the approximation quality of the selected PDMs. The examined estimates refer to the inconsistency of various Pairwise Comparison Matrices (PCMs)—i.e., W = (wij), wij > 0, where i, j = 1,…, n—which are obtained during the pairwise comparison simulation process examined with the application of Wolfram’s Mathematica Software. Thus, theoretical considerations are accompanied by Monte Carlo simulations that apply various scenarios for the PCM perturbation process and are designed for hypothetical three-level AHP frameworks. The examination results show the similarities and discrepancies among the examined PDMs from the perspective of their quality, which enriches the state of knowledge about the examined PCB prioritization methodology and provides further prospective opportunities.

Theory and practice of neural networks application to building mathematical model of centrifugal compressor vane diffusers

Optimal design of centrifugal compressor stages needs special computational and experimental methods, both of them could be costly enough. So new advanced design methods which can provide optimal solution faster are needed. Authors developed the set of mathematical models – Universal modeling method - for describing compressor stages characteristics. Its models are being widened and improved. One the most advanced approaches of model building is based on machine learning. A neural network based method for predicting centrifugal compressor vane diffuser characteristics was developed. Input data for network training was obtained from CFD simulations. The resulting model for diffuser loss coefficient shows good approximation quality and can be used for improvement of VD model in Universal modeling method.

Reachability Deficits in Quantum Approximate Optimization of Graph Problems

The quantum approximate optimization algorithm (QAOA) has become a cornerstone of contemporary quantum applications development. Here we show that the density of problem constraints versus problem variables acts as a performance indicator. Density is found to correlate strongly with approximation inefficiency for fixed depth QAOA applied to random graph minimization problem instances. Further, the required depth for accurate QAOA solution to graph problem instances scales critically with density. Motivated by Google's recent experimental realization of QAOA, we preform a reanalysis of the reported data reproduced in an ideal noiseless setting. We found that the reported capabilities of instances addressed experimentally by Google, approach a rapid fall-off region in approximation quality experienced beyond intermediate-density. Our findings offer new insight into performance analysis of contemporary quantum optimization algorithms and contradict recent speculation regarding low-depth QAOA performance benefits.

Piecewise linear approximations for hydropower production function applied on the hydrothermal unit commitment problem

In a centralized-based dispatch market, the network-constrained hydrothermal unit commitment (NCHTUC) determines generation decisions that minimize the expected operating cost, usually in a day-ahead planning horizon. In this context, hydropower offers unique flexibility features, and an adequate representation is crucial for overcoming operation challenges. However, due to computational burden, simplifications on hydropower production function (HPF) modeling are necessary since NCHTUC is a large-scale nonlinear discrete optimization problem. The plant-based HPF piecewise linear approach is the most common simplification presented for real-life cases. In this case, the generating units (GUs) of a plant are aggregated and represented by a single equivalent generator. Although this approach reduces the size and the complexity of the NCHTUC significantly, several operating issues are not considered adequately, especially the forbidden zones and the nonlinearities of the GUs. In this paper, we present a new approach that considers the nonlinearities and forbidden zones of the HPF via aggregation of the GUs and piecewise mixed-integer linear approximation in an innovative way. Experiments are performed in a modified version of the IEEE 118-bus system to verify the approach’s effectiveness, analyzed in terms of the relation between computation burden and the HPF approximation quality.

ALPS: A Unified Framework for Modeling Time Series of Land Ice Changes

Modeling time series (TS) is a research focus in cryospheric sciences because of the complexity and multiscale nature of events of interest. Highly nonuniform sampling of measurements from different sensors with different levels of accuracy, as is typical for measurements of ice sheet elevations, makes the problem even more challenging. In this article, we propose a spline-based approximation framework (ALPS—Approximation by Localized Penalized Splines) for modeling TS of land ice changes. The localized support of the B-spline basis functions enable robustness to nonuniform sampling, a considerable improvement over other global and piecewise local models. With features like discrete-coordinate-difference-based penalization and two-level outlier detection, ALPS further guarantees the stability and quality of approximations. ALPS incorporates rigorous model uncertainty estimates with all approximations. As demonstrated by examples, ALPS performs well for a variety of data sets, including TS of ice sheet thickness, elevation, velocity, and terminus locations. The robust estimation of TS and their derivatives facilitates new applications, such as the reconstruction of high-resolution elevation change records by fusing sparsely sampled TS of ice sheet thickness changes with modeled firn thickness changes, and the analysis of the relationship between different outlet glacier observations to gain new insights into processes and forcing.