Parameters Of Problem Research Articles

An integrated stochastic model and algorithm for constrained multi-item newsvendor problems by two-stage decision-making approach

In this paper, we first build a model for a complicated newsvendor problem, which is involved with optimization to preseason production quantity with random yields and outsourcing quantity with supplier quantity discounts. Different from the existing models, a two-stage decision-making strategy is proposed for the retailer such that the possible loss, caused by the uncertainty of production, is reduced in virtue of dynamically choosing the outsourcing quantities and sales prices. Additionally, the cross-elasticity of prices is incorporated into formulating the random and price-dependent demand function in our model. Consequently, the mathematical model is expressed by a parameterized optimization problem, and a cyclic coordinate descent algorithm (CCD), combined with a feasible direction method and a parameters-choosing procedure, is developed to obtain optimal solutions of the original problem. Both substitute and complementary scenarios for different products are further investigated by numerical simulation. Main managerial implications are summarized as follows: (1) The two-stage policy of decision-making brings more benefits than a static policy for the complicated newsvendor problems. (2) The budget available is a critical constraint for the small-and-medium sized business, the maximum expected profit increases as the budget becomes greater, but remains at the same level after it reaches a threshold value. (3) The cross elasticity relationship between the products plays a critical role in choosing optimal quantities of preseason production, sales prices and outsourcing quantities.

Multiplicative Parameterization Above a Guarantee

Parameterization above a guarantee is a successful paradigm in Parameterized Complexity. To the best of our knowledge, all fixed-parameter tractable problems in this paradigm share an additive form defined as follows. Given an instance ( I,k ) of some (parameterized) problem π with a guarantee g(I) , decide whether I admits a solution of size at least (or at most) k + g(I) . Here, g(I) is usually a lower bound on the minimum size of a solution. Since its introduction in 1999 for M AX SAT and M AX C UT (with g(I) being half the number of clauses and half the number of edges, respectively, in the input), analysis of parameterization above a guarantee has become a very active and fruitful topic of research. We highlight a multiplicative form of parameterization above (or, rather, times) a guarantee: Given an instance ( I,k ) of some (parameterized) problem π with a guarantee g(I) , decide whether I admits a solution of size at least (or at most) k · g(I) . In particular, we study the Long Cycle problem with a multiplicative parameterization above the girth g(I) of the input graph, which is the most natural guarantee for this problem, and provide a fixed-parameter algorithm. Apart from being of independent interest, this exemplifies how parameterization above a multiplicative guarantee can arise naturally. We also show that, for any fixed constant ε > 0, multiplicative parameterization above g(I) 1+ε of Long Cycle yields para-NP-hardness, thus our parameterization is tight in this sense. We complement our main result with the design (or refutation of the existence) of fixed-parameter algorithms as well as kernelization algorithms for additional problems parameterized multiplicatively above girth.

Verifying safety of synchronous fault-tolerant algorithms by bounded model checking

Threshold automata are a formalism introduced for modeling, verification, and synthesis of fault-tolerant distributed algorithms for asynchronous systems, that is, in interleaving semantics. Owing to well-known limitations of what can be achieved in purely asynchronous systems, many fault-tolerant distributed algorithms are designed for synchronous or round-based semantics. In this paper, we introduce the synchronous variant of threshold automata and study their applicability and limitations for the verification of synchronous fault-tolerant distributed algorithms. We show that the parameterized reachability problem for synchronous threshold automata is undecidable. Still, we show that many synchronous fault-tolerant distributed algorithms have a bounded diameter, even though the algorithms are parameterized by the number of processes. Hence, bounded model checking can be used for verifying these algorithms. The existence of bounded diameters is the main conceptual insight in this paper. We compute the diameter of several algorithms and check their safety properties, using SMT queries that contain quantifiers for dealing with the parameters symbolically. Surprisingly, performance of the SMT solvers on these queries is very good, reflecting the recent progress in dealing with quantified queries. We found that the diameter bounds of synchronous algorithms in the literature are tiny (from 1 to 8), which makes our approach applicable in practice. For a specific class of algorithms, we also establish a theoretical result on the existence of a diameter, providing a first explanation for our experimental results.

Refined notions of parameterized enumeration kernels with applications to matching cut enumeration

An enumeration kernel as defined by Creignou et al. (2017) [11] for a parameterized enumeration problem consists of an algorithm that transforms each instance into one whose size is bounded by the parameter plus a solution-lifting algorithm that efficiently enumerates all solutions from the set of the solutions of the kernel. We propose to consider two new versions of enumeration kernels by asking that the solutions of the original instance can be enumerated in polynomial time or with polynomial delay from the kernel solutions. Using the NP-hard Matching Cut problem parameterized by structural parameters such as the vertex cover number or the cyclomatic number of the input graph, we show that the new enumeration kernels present a useful notion of data reduction for enumeration problems which allows to compactly represent the set of feasible solutions.

MESHLESS GALERKIN METHOD BASED ON RBFS AND REPRODUCING KERNEL FOR QUASI-LINEAR PARABOLIC EQUATIONS WITH DIRICHLET BOUNDARY CONDITIONS

The main aim of this paper is to present a hybrid scheme of both meshless Galerkin and reproducing kernel Hilbert space methods. The Galerkin meshless method is a powerful tool for solving a large class of multi-dimension problems. Reproducing kernel Hilbert space method is an extremely efficient approach to obtain an analytical solution for ordinary or partial differential equations appeared in vast areas of science and engineering. The error analysis and convergence show that the proposed mixed method is very efficient. Since the solution space spanned by radial basis functions do not directly satisfy essential boundary conditions, an auxiliary parameterized technique is employed. Theoretical studies indicate that this new method is very stable, though a parameterized problem is employed instead of the main problem.

A Riemannian under-determined BFGS method for least squares inverse eigenvalue problems

This paper is concerned with the parameterized least squares inverse eigenvalue problems for the case that the number of parameters to be constructed is greater than the number of prescribed realizable eigenvalues. Intrinsically, this is a specific problem of finding a zero of an under-determined nonlinear map defined between a Riemannian product manifold and a matrix space. To solve this problem, we propose a Riemannian under-determined BFGS algorithm with a specialized update formula for iterative linear operators, and an Armijo type line search is used. Global convergence properties of this algorithm are established under some mild assumptions. In addition, we also generalize a Riemannian inexact Newton method for solving this problem. Specially, the explicit form of the inverse of the linear operator corresponding to the perturbed normal Riemannian Newton equation is obtained, which improves the efficiency of Riemannian inexact Newton method. Numerical experiments are provided to illustrate the efficiency of the proposed method.

A Fitted Second-Order Difference Method for a Parameterized Problem with Integral Boundary Condition Exhibiting Initial Layer

In this paper, the homogeneous type fitted difference scheme for solving singularly perturbed problem depending on a parameter with integral boundary condition is proposed. We prove that the method is $$O(N^{-2}\ln N)$$ uniform convergent on Shishkin meshes. Numerical results are also presented.

A non-intrusive model order reduction approach for multi-physics parametrized problems - Application to induction heating process

Finite element modeling (FEM) has recently become the most attractive computational tool to predict and optimize many industrial problems. However, the FEM becomes ineffective as far as complex multi-physics parameterized problems, such as induction heating process, are concerned because of high computational cost. This work aims at studying the possibility of applying a new approach based on the reduced order modeling (ROM) to obtain approximate solutions of a parametric problem. Basically, the effect of induction heating process parameters on some physical quantities of interest (QoI) will be analyzed under the real-time constraint. To achieve this dimensionality reduction, a set of precomputed solutions is first collected, at some sparse points in the space domain and for a properly selected process parameters, by solving the full-order models implemented in the commercial finite element software FORGE®. A Proper Orthogonal Decomposition (POD) based reducedorder model is then applied to the collected data to find a low dimensional space onto which the solution manifold could be projected and an approximated solution for new process parameters could be efficiently computed in real time. Besides, the POD is applied to build a reduced basis and to compute their corresponding modal coefficients. It is then followed by artificial intelligence techniques for regression purpose, such as sparse Proper Generalized Decomposition, to fit the low dimensional POD modal coefficients. Hence, the problem can be solved with a much lower dimension compared to the initial one. It was shown that a good approximation of the QoI was provided, in low-data limit, using a single POD modal coefficient as a response for the regression methods. However, the obtained approximation accuracy needs to be enhanced.

On the Descriptive Complexity of Color Coding

Color coding is an algorithmic technique used in parameterized complexity theory to detect “small” structures inside graphs. The idea is to derandomize algorithms that first randomly color a graph and then search for an easily-detectable, small color pattern. We transfer color coding to the world of descriptive complexity theory by characterizing—purely in terms of the syntactic structure of describing formulas—when the powerful second-order quantifiers representing a random coloring can be replaced by equivalent, simple first-order formulas. Building on this result, we identify syntactic properties of first-order quantifiers that can be eliminated from formulas describing parameterized problems. The result applies to many packing and embedding problems, but also to the long path problem. Together with a new result on the parameterized complexity of formula families involving only a fixed number of variables, we get that many problems lie in FPT just because of the way they are commonly described using logical formulas.

Non-intrusive reduced-order modeling for uncertainty quantification of space–time-dependent parameterized problems

We propose a non-intrusive reduced-order modeling method for space–time-dependent parameterized problems in the context of uncertainty quantification. In the offline stage, proper orthogonal decomposition (POD) is used to extract the spatial modes based on a set of high-fidelity time-parameter-dependent snapshots and then to extract the temporal modes of the projection coefficients of the spatial modes. Finally, parameter-dependent combination coefficients are approximated using polynomial chaos expansions (PCEs). Cubic spline interpolation is used to evaluate the temporal modes at any other given time. In the online stage, a fast evaluation is provided at any given time and parameter by simply estimating the values of the polynomials and temporal modes. To validate the numerical performance of the proposed method, three time-dependent parameterized problems are tested: a one-dimensional (1-D) forced Burger’s equation with a random force term, a 1-D diffusion–reaction equation with a random field force term, and a two-dimensional incompressible fluid flow over a cylinder with a random inflow boundary condition. The results indicate that the proposed method is able to approximate the full-order model very inexpensively with a reasonable loss of accuracy for problems with uncorrelated or correlated input parameters. Furthermore, the proposed method is effective in estimating low-order moments, indicating that it has great potential for use in uncertainty quantification analysis of space–time-dependent problems.

Bifidelity Data-Assisted Neural Networks in Nonintrusive Reduced-Order Modeling

In this paper, we present a new nonintrusive reduced basis method when a cheap low-fidelity model and an expensive high-fidelity model are available. The method employs proper orthogonal decomposition method to generate the high-fidelity reduced basis and a shallow multilayer perceptron to learn the high-fidelity reduced coefficients. In contrast to previously proposed methods, besides the model parameters, we also augmented the features extracted from the data generated by an efficient bi-fidelity surrogate developed in Narayan et al. (SIAM J Sci Comput 36(2):A495–A521, 2014) and Zhu et al. (SIAM/ASA J Uncertain Quantif 2(1):444–463, 2014) as the input feature of the proposed neural network. By incorporating relevant bi-fidelity features, we demonstrate that such an approach can improve the predictive capability and robustness of the neural network via several benchmark examples. Due to its nonintrusive nature, it is also applicable to general parameterized problems.

Parameterized complexity of completeness reasoning for conjunctive queries

Incompleteness management has become a popular research topic and been viewed in many applications in the area of data quality and data management. Traditional methods for handling incompleteness assume data is totally complete or incomplete. However, in practical applications, data is often partial complete, which means that data is not totally complete but some special parts of the data satisfying given semantic specifications are complete. Intuitively, partial complete data can still give complete answers for queries consistent with the semantic specifications. Therefore, it is highly needed to study the fundamental problems for managing partial complete data. However, as far as known by us, there are only few works focusing on this area. The most important and fundamental problem, completeness reasoning, is studied from the aspect of parameterized complexity by this paper. The completeness reasoning problem, TC-QC (Table Completeness to Query Completeness), is first formally defined and studied by Razniewski et al. [1]. Given completeness statements of data, the goal of the TC-QC problem is to determine whether the result of a special query Q is complete, that is to reason query completeness based on given data completeness. Razniewski et al. have shown that the TC-QC problem is NP-hard even for conjunctive queries, and a natural and interesting question is whether or not TC-QC can be solved efficiently by parameterized algorithms. To answer that, the parameterized complexities of completeness reasoning for conjunctive queries are studied by the paper. First, it is shown that, considering the parameterizations defined by the size of query completeness or table completeness, the parameterized TC-QC problem for conjunctive queries is para-NP-complete, which strongly indicate that the TC-QC problems parameterized by the above two parameters do not admit fixed-parameter tractable algorithms. Then, for more special cases parameterized by different constraints on query structures like degree, tree-width and number of variables, the TC-QC problems are still not fixed parameter tractable. Finally, on the positive side, if each data completeness statement has a constant size bound, the parameterized TC-QC problem defined by the query completeness size can be solved by a fixed-parameter tractable algorithm.

Combining the cross-entropy algorithm and ∈-constraint method for multiobjective optimization

Abstract This paper aims to propose a new hybrid approach for solving multiobjective optimization problems. This approach is based on a combination of global and local search procedures. The cross-entropy method is used as a stochastic model-based method to solve the multiobjective optimization problem and reach a first elite set of global solutions. In the local search step, an ∈-constraint method converts the multiobjective optimization problem to a series of parameterized single-objective optimization problems. Then, sequential quadratic programming (SQP) is used to solve the derived single-objective optimization problems allowing to reinforce and improve the global results. Numerical examples are used to demonstrate the efficiency and effectiveness of the proposed approach.

Parametric Problems in Power System Analysis: Recent Applications of Polynomial Approximation Based on Galerkin Method

In power systems, there are many uncertainty factors such as power outputs of distributed generations and fluctuations of loads. It is very beneficial to power system analysis to acquire an explicit function describing the relationship between these factors (namely parameters) and power system states (or performances). This problem, termed as parametric problem (PP) in this paper, can be solved by Galerkin method, which is a powerful and mathematically rigorous method aiming to seek an accurate explicit approximate function by projection techniques. This paper provides a review of the applications of polynomial approximation based on Galerkin method in power system PPs as well as stochastic problems. First, the fundamentals of polynomial approximation and Galerkin method are introduced. Then, the process of solving three types of typical PPs by polynomial approximation based on Galerkin method is elaborated. Finally, some application examples as well as several potential applications of power system PPs solved by Galerkin method are presented, namely the probabilistic power flow, approximation of static voltage stability region boundary, and parametric time-domain dynamic simulation.

A Scalable Buffer Queue Sizing Algorithm for Latency Insensitive Systems

Timing violations in high performance communication channels in system-on-chips (SoC) may occur in the late stages of the physical design process. To address that, latency insensitive systems (LISs) employ pipelining in the communication channels through the insertion of relay stations. Although the functionality of an LIS is robust with respect to the communication latencies, imbalances in relay station insertion may degrade the throughput of the system. While having a large number of buffer queues can eliminate such performance loss, the system may not have adequate area to accommodate these buffers. The problem of buffer queue sizing for maximizing throughput while meeting buffer area constraints has been solved using a mixed-integer linear program (MILP) formulation; however, such an approach is not scalable. In this work, we formulate the buffer queue sizing problem as a parameterized graph optimization problem where for every communication channel there is a parameterized edge with buffer counts as the edge weight. We then use a minimum cycle mean algorithm to determine from which edges buffers can be removed safely. Experimental results on large LISs suggest that the proposed approach is scalable. Moreover, quality of the solutions, in terms of the throughput and the size of buffer queues, is observed to be as good as that of the MILP-based approach.

An improved a posteriori error estimation for a parameterized singular perturbation problem

In this paper a hybrid difference method is used to approximate a parameterized singular perturbation problem. An improved a posteriori error estimation for the difference scheme on an arbitrary mesh is given. A solution-adaptive algorithm based on the a posteriori error estimation is designed by equidistributing a monitor function. Numerical experiments show that the scheme is second-order uniformly convergent and improves previous results.

Multi-fidelity and multi-objective optimization of low-thrust transfers with control strategy for all-electric geostationary satellites

To reduce the transfer time and radiation damage of all-electric geostationary satellites, this article develops a metamodel based multi-objective optimization scheme for low-thrust geostationary Earth orbit transfers. A multi-stage control strategy is employed to convert the optimal Earth orbital transfer problem into a parameterized multi-objective optimization problem with minimum total transfer time and proton displacement dose objectives. Based on the notion of multi-model fusion, the multi-fidelity system dynamics and radiation damage models are established to reduce the optimization cost. Furthermore, a novel adaptive Co-Kriging based multi-objective optimization method is proposed to effectively solve the multi-objective optimization problem with multi-fidelity models. In this approach, Co-Kriging metamodels are constructed using the multi-fidelity data to approximate the time-consuming low-thrust transfer models for optimization. And the Co-Kriging metamodels are adaptively refined by the infill samples to explore the Pareto frontier efficiently. Finally, a real-world all-electric geostationary satellite transfer example is investigated. The results show that a number of non-dominated low-thrust trajectories are successfully obtained with limited computational budget. Moreover, the total transfer time and displacement damage dose are significantly reduced after optimization, which illustrates the effectiveness of the metamodel based multi-objective low-thrust transfer optimization scheme.

On computation of optimal strategies in oligopolistic markets respecting the cost of change

The paper deals with a class of parameterized equilibrium problems, where the objectives of the players do possess nonsmooth terms. The respective Nash equilibria can be characterized via a parameter-dependent variational inequality of the second kind, whose Lipschitzian stability, under appropriate conditions, is established. This theory is then applied to evolution of an oligopolistic market in which the firms adapt their production strategies to changing input costs, while each change of the production is associated with some “costs of change”. We examine both the Cournot-Nash equilibria as well as the two-level case, when one firm decides to take over the role of the Leader (Stackelberg equilibrium). The impact of costs of change is illustrated by academic examples.

Reduced order methods for parametric optimal flow control in coronary bypass grafts, toward patient-specific data assimilation.

Coronary artery bypass grafts (CABG) surgery is an invasive procedure performed to circumvent partial or complete blood flow blockage in coronary artery disease. In this work, we apply a numerical optimal flow control model to patient-specific geometries of CABG, reconstructed from clinical images of real-life surgical cases, in parameterized settings. The aim of these applications is to match known physiological data with numerical hemodynamics corresponding to different scenarios, arisen by tuning some parameters. Such applications are an initial step toward matching patient-specific physiological data in patient-specific vascular geometries as best as possible. Two critical challenges that reportedly arise in such problems are: (a) lack of robust quantification of meaningful boundary conditions required to match known data as best as possible and (b) high computational cost. In this work, we utilize unknown control variables in the optimal flow control problems to take care of the first challenge. Moreover, to address the second challenge, we propose a time-efficient and reliable computational environment for such parameterized problems by projecting them onto a low-dimensional solution manifold through proper orthogonal decomposition-Galerkin.

On higher-order proto-differentiability and higher-order asymptotic proto-differentiability of weak perturbation maps in parametric vector optimization

The main purpose of this paper is to study higher-order sensitivity analysis in parametric vector optimization problems. Firstly, the higher-order proto-differentiability/the higher-order asymptotic proto-differentiability of the feasible map of a parametric vector optimization problem are considered. Then, we verify that the weak efficient solution map and the weak perturbation map of a parameterized vector optimization problem are higher-order proto-differentiable/higher-order asymptotic proto-differentiable under some suitable qualification conditions.