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This paper proposes an algorithm to obtain the sampling factors to model any frequency partitioning so that it is realized using low complexity rational decimated non-uniform filter banks (RDNUFBs). The proposed algorithm is employed to approximate the Bark scale to find a rational frequency partitioning that can be realized using RDNUFBs with less approximation error. The proposed Bark frequency partitioning is found to reduce the average deviation in centre frequency and bandwidth by 65.04% and 48.50%, respectively, and root-mean-square bandwidth deviation by 54.46% when compared to those of the existing perceptual wavelet approximation of Bark scale. In the first filter bank design approach discussed in the paper, a near-perfect reconstruction partially cosine modulated filter bank is employed to obtain the improved Bark frequency partitioning. In the second design approach, the hardware complexity of the PCM-based RDNUFB is reduced by deriving the channels with different sampling factors from the same prototype filter by the method of channel merging. There is a considerable reduction of 40.83% in the number of multipliers when merging of partially cosine modulated channels is employed. It is also found that both the proposed approaches can reduce the delay by 95.17% when compared to the existing rational tree approximation methods and hence, are suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Tang–Gong interleaved sequences constructed from the generalized GMW sequence pair are a class of binary sequences with optimal autocorrelation magnitude. In this paper, the symmetric 2-adic complexity of these sequences is investigated. We first derive a lower bound on their 2-adic complexity by extending the method proposed by Hu. Then, by analysing the algebraic structure of these sequences, a lower bound on their symmetric 2-adic complexity is obtained. Our result shows that the symmetric 2-adic complexity of these sequences is large enough to resist attacks with the rational approximation algorithm. [ABSTRACT FROM AUTHOR]

Optimization of conflicting functions is of paramount importance in decision making, and real world applications frequently involve data that is uncertain or unknown, resulting in multi-objective optimization (MOO) problems of stochastic type. We study the stochastic multi-gradient (SMG) method, seen as an extension of the classical stochastic gradient method for single-objective optimization. At each iteration of the SMG method, a stochastic multi-gradient direction is calculated by solving a quadratic subproblem, and it is shown that this direction is biased even when all individual gradient estimators are unbiased. We establish rates to compute a point in the Pareto front, of order similar to what is known for stochastic gradient in both convex and strongly convex cases. The analysis handles the bias in the multi-gradient and the unknown a priori weights of the limiting Pareto point. The SMG method is framed into a Pareto-front type algorithm for calculating an approximation of the entire Pareto front. The Pareto-front SMG algorithm is capable of robustly determining Pareto fronts for a number of synthetic test problems. One can apply it to any stochastic MOO problem arising from supervised machine learning, and we report results for logistic binary classification where multiple objectives correspond to distinct-sources data groups. [ABSTRACT FROM AUTHOR]

We study the computational complexity of (deterministic or randomized) algorithms based on point samples for approximating or integrating functions that can be well approximated by neural networks. Such algorithms (most prominently stochastic gradient descent and its variants) are used extensively in the field of deep learning. One of the most important problems in this field concerns the question of whether it is possible to realize theoretically provable neural network approximation rates by such algorithms. We answer this question in the negative by proving hardness results for the problems of approximation and integration on a novel class of neural network approximation spaces. In particular, our results confirm a conjectured and empirically observed theory-to-practice gap in deep learning. We complement our hardness results by showing that error bounds of a comparable order of convergence are (at least theoretically) achievable. [ABSTRACT FROM AUTHOR]

Minimizing the weighted completion time of jobs in the unrelated parallel machines model is a fundamental scheduling problem. The first (3 / 2 - c) –approximation algorithm for this problem, for some constant c > 0 , was obtained in the work of Bansal et al. (SIAM J Comput, 2021). A key ingredient in this work was the first dependent-rounding algorithm with a certain guaranteed amount of negative correlation. We improve upon this guaranteed amount from 1/108 to 1/27, thus also improving upon the constant c in the algorithms of Bansal et al. and Li (SIAM J Comput, 2020) for weighted completion time. Given the now-ubiquitous role played by dependent rounding in scheduling and combinatorial optimization, our improved dependent rounding is also of independent interest. [ABSTRACT FROM AUTHOR]

Variational Quantum Algorithms (VQAs) have made great success in the Noisy Intermediate-Scale Quantum (NISQ) era due to their relative resilience to noise and high flexibility relative to quantum resources. Quantum Architecture Search (QAS) aims to enhance the performance of VQAs by refining the structure of the adopted Parameterized Quantum Circuit (PQC). QAS is garnering increased attention owing to its automation, reduced reliance on expert experience, and its ability to achieve better performance while requiring fewer quantum gates than manually designed circuits. However, existing QAS algorithms optimize the structure from scratch for each VQA without using any prior experience, rendering the process inefficient and time-consuming. Moreover, determining the number of quantum gates, a crucial hyper-parameter in these algorithms is a challenging and time-consuming task. To mitigate these challenges, we accelerate the QAS algorithm via a meta-trained generator. The proposed algorithm directly generates high-performance circuits for a new VQA by utilizing a meta-trained Variational AutoEncoder (VAE). The number of quantum gates required in the designed circuit is automatically determined based on meta-knowledge learned from a variety of training tasks. Furthermore, we have developed a meta-predictor to filter out circuits with suboptimal performance, thereby accelerating the algorithm. Simulation results on variational quantum compiling and Quantum Approximation Optimization Algorithm (QAOA) demonstrate the superior performance of our method over a state-of-the-art algorithm, namely Differentiable Quantum Architecture Search (DQAS). [ABSTRACT FROM AUTHOR]

We consider the weighted completion time minimization problem for capacitated parallel machines, which is a fundamental problem in modern cloud computing environments. In our setting, the processed jobs may be of varying duration, require different resources, and be of unequal importance (weight). Each server (machine) can process multiple concurrent jobs up to its capacity. We study heuristic approaches with provable approximation guarantees and offer an algorithm that prioritizes the jobs with the smallest volume-by-weight ratio. We bound the algorithm's approximation ratio using a decreasing function of the ratio between the highest resource demand of any job and the server's capacity. Thereafter, we create a hybrid, constant approximation algorithm for two or more machines. We also develop a constant approximation algorithm for the case of a single machine. Via a numerical study and a mixed-integer linear program of the problem, we demonstrate the performance of the suggested algorithm with respect to the optimal solutions and alternative scheduling methods. We show that the suggested scheduling method can be applied to both offline and online problems that may arise in real-world settings. This research is the first, to the best of our knowledge, to propose a polynomial-time algorithm with a constant approximation ratio for minimizing the weighted sum of job completion times for capacitated parallel machines. [ABSTRACT FROM AUTHOR]

We study the synchronization properties of a generic networked dynamical system, and show that, under a suitable approximation, the transition to synchronization can be predicted with the only help of eigenvalues and eigenvectors of the graph Laplacian matrix. The transition comes out to be made of a well defined sequence of events, each of which corresponds to a specific clustered state. The network's nodes involved in each of the clusters can be identified, and the value of the coupling strength at which the events are taking place can be approximately ascertained. Finally, we present large-scale simulations which show the accuracy of the approximation made, and of our predictions in describing the synchronization transition of both synthetic and real-world large size networks, and we even report that the observed sequence of clusters is preserved in heterogeneous networks made of slightly non-identical systems. Natural and synthetic networked systems can be characterized by synchrony or asynchrony of groups of their nodes (clusters) and undergo different scenarios of synchronization transitions. The authors propose an approach to predict the entire sequence of events that are taking place during the synchronization transition in networks of identical elements with any preferrable architecture. [ABSTRACT FROM AUTHOR]

We study two scheduling problems in a proportionate flow shop environment, where job processing times are machine independent. In contrast to classical proportionate flow shop models, we assume (in both problems) that processing times are step-deteriorating. Accordingly, each job J j has a normal processing time, a j , if it starts to be processed in the shop no later than its deteriorating date, δ j . Otherwise, the job's processing time increases by b j (the job's deterioration penalty). Our aim is to find a job schedule that minimizes either the makespan or the total load. These two problems are known to be N P -hard for the special case of a single machine, even when all jobs have the same deteriorating date. In this paper, we derive several positive results in relation to the two problems. We first show that the two problems can be represented in a unified way. We then prove that the unified problem is only ordinary N P -hard by providing a pseudo-polynomial time algorithm for its solution. We also show that the pseudo-polynomial time algorithm can be converted into a fully polynomial time approximation scheme (FPTAS). Finally, we analyze the parameterized complexity of the problem with respect to the number of different deteriorating dates in the instance, v δ . We show that although the problem is N P -hard when v δ = 1 , it is fixed parameterized tractable (FPT) for the combined parameters (i) (ν δ , ν a) and (ii) (ν δ , ν b) , where ν a is the number of different normal processing times in the instance, and ν b is the number of different deterioration penalties in the instance. [ABSTRACT FROM AUTHOR]

This work studies solutions to determine the macro-elastic moduli of three-phase composite materials with imperfect interfaces in 2D. Which is based on the coated circular assemblage model of Hashin-Strikman and the polarization approximation method (PA) to develop the formulae for elastic moduli of circle inclusions with spring-layer and surface-stress imperfect interfaces. From that, explicit algebraic expressions were obtained to estimate the elastic moduli of three-phase composites with imperfect interfaces, in which two phases are different with circular inclusions distributed randomly in the matrix. Besides, the FFT algorithm and the differential approximation (DA) are also developed to determine the elastic moduli of the three-phase composite with imperfect interfaces. The results of the FFT numerical methods will be compared with the DA and PA results with different material cases to show the effectiveness of the applied methods. [ABSTRACT FROM AUTHOR]

We investigate the seemingly untouched yet the most general parallel identical k-stage flow-shops scheduling, in which we are given an arbitrary number of indistinguishable k-stage flow-shops and a set of jobs each to be processed on one of the flow-shops, and the goal is to schedule these jobs to the flow-shops so as to minimize the makespan. Here the number k of stages is a fixed constant, but the number of flow-shops is part of the input. This scheduling problem is strongly NP-hard. To the best of our knowledge all previously presented approximation algorithms are for the special case where k = 2 , including a 17/6-approximation in 2017, a (2 + ϵ) -approximation in 2018, and the most recent polynomial-time approximation scheme in 2020; they all take advantage of the number of stages being two. We deal with an arbitrary constant k ≥ 3 , where the k-stage flow-shop scheduling is already strongly NP-hard. To define a configuration that summarizes the key information about the job assignments in a feasible schedule, we present novel concepts of big job type, big job assignment pair type, and flow-shop type. We show that the total number of distinct configurations is a polynomial in the input number of flow-shops. We then present how to compute a schedule for each configuration that assigns all the big jobs, followed by how to allocate all the small jobs into the schedule at a cost only a fraction of the makespan. These together lead to a polynomial-time approximation scheme for the problem. [ABSTRACT FROM AUTHOR]

We consider single-machine scheduling with a non-renewable resource. In this setting, we are given a set of jobs, each characterized by a processing time, a weight, and a resource requirement. At fixed points in time, certain amounts of the resource are made available to be consumed by the jobs. The goal is to assign the jobs non-preemptively to time slots on the machine, so that each job has enough resource available at the start of its processing. The objective that we consider is the minimization of the sum of weighted completion times. The main contribution of the paper is a PTAS for the case of 0 processing times ( 1 | r m = 1 , p j = 0 | ∑ w j C j ). In addition, we show strong NP-hardness of the case of unit resource requirements and weights ( 1 | r m = 1 , a j = 1 | ∑ C j ), thus answering an open question of Györgyi and Kis. We also prove that the schedule corresponding to the Shortest Processing Time First ordering provides a 3/2-approximation for the latter problem. Finally, we investigate a variant of the problem where processing times are 0 and the resource arrival times are unknown. We present a (4 + ϵ) -approximation algorithm, together with a (4 - ε) -inapproximability result, for any ε > 0 . [ABSTRACT FROM AUTHOR]

This paper considers several two-machine open shop problems with two agents. Each agent has an independent set of nonpreemptive jobs, and the objective is to find either a schedule minimizing a linear combination of the makespans of both agents, a schedule minimizing the makespan of one agent with the makespan of the other agent not exceeding a threshold, or all Pareto-optimal schedules with respect to the makespans of both agents. We present a number of results for the problems above, including a polynomial algorithm and a pseudo-polynomial algorithm for special cases, non-approximability, two approximation algorithms, and a fully polynomial-time approximation scheme. Finally, we run numerical experiments to show the effectiveness of the pseudo-polynomial algorithm and the approximation algorithms. [ABSTRACT FROM AUTHOR]

In this paper we analyze the links between the agent's reservation utility, bargaining power, and risk aversion in terms of their simultaneous effects on the structure of optimal static contracts. We compare the following principal-agent models in the symmetric and asymmetric information environments: the standard approach, which includes a participation constraint, and a multi-objective (MO) optimization approach in which the objective function is a convex combination of the expected utilities of the principal and the agent. The MO model does not include a participation constraint, but it includes a parameter for the agent's bargaining power. We also study an Evolutionary Algorithm implementation of the static principal-agent model to support and extend our analytical results. We show that the numerical solution approximated by our implementation of an evolutionary algorithm is in line with the analytical solutions mentioned before. That is, for every admissible value of the agent's reservation utility, there is a corresponding admissible value of the agent's bargaining parameter, both in the MO approach and the EA implementation. [ABSTRACT FROM AUTHOR]

We revisit a classical scheduling model to incorporate modern trends in data center networks and cloud services. Addressing some key challenges in the allocation of shared resources to user requests (jobs) in such settings, we consider the following variants of the classic resource allocation problem (RAP). The input to our problems is a set J of jobs and a set M of homogeneous hosts, each has an available amount of some resource. Assuming that time is slotted, a job is associated with a release time, a due date, a weight and a given length, as well as its resource requirement. A feasible schedule is an allocation of the resource to a subset of the jobs, satisfying the job release times/due dates as well as the resource constraints. A crucial distinction between classic RAP and our problems is that we allow preemption and migration of jobs, motivated by virtualization techniques. We consider two natural objectives: throughput maximization (MaxT), which seeks a maximum weight subset of the jobs that can be feasibly scheduled on the hosts in M, and resource minimization (MinR), that is finding the minimum number of (homogeneous) hosts needed to feasibly schedule all jobs. Both problems are known to be NP-hard. We first present an Ω (1) -approximation algorithm for MaxT instances where time-windows form a laminar family of intervals. We then extend the algorithm to handle instances with arbitrary time-windows, assuming there is sufficient slack for each job to be completed. For MinR we study a more general setting with d resources and derive an O (log d) -approximation for any fixed d ≥ 1 , under the assumption that time-windows are not too small. This assumption can be removed leading to a slightly worse ratio of O (log d log ∗ T) , where T is the maximum due date of any job. [ABSTRACT FROM AUTHOR]

We consider bicriteria flowshop scheduling problems with two machines to simultaneously minimize the makespan and the total completion time without any prioritization between the two objectives. An approximation relative to the optimal value of the problem with regard to each objective is adopted, even though a schedule with both objectives reaching their minimum at the same time may not exist. We consider several problems with ordered aspects on processing times such as job ordered and machine ordered. For each problem, we propose a fast (ρ 1 , ρ 2) -approximation algorithm, where ρ 1 and ρ 2 indicate the approximation ratios with regard to the makespan and the total completion time, respectively, and we explore the problem's inapproximability region that cannot be reached by any algorithm. [ABSTRACT FROM AUTHOR]

In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high-quality solutions. However, as we are tackling NP-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where (depth-1) RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for complex problems. [ABSTRACT FROM AUTHOR]

This study addresses the energy efficiency challenge in cloud data centers by examining the Virtual Machine Placement (VMP) problem. VMP involves mapping virtual machines (VMs) to physical machines (PMs) under capacity constraints. The paper focuses on the bin packing with linear usage cost (BPLUC) variant of bin packing, which includes fixed and variable costs in the calculation of the cost of a used bin. We prove that every approximation algorithm for the bin and vector bin packing can be used for BPLUC and VBPLUC, respectively. We propose a more power-efficient approach to VMP by applying a vector bin packing algorithm to minimize power consumption in data centers. We test the proposed algorithm on various synthetic and real workloads, and the experimental results demonstrate that it is more power-efficient than existing algorithms for VMP. The findings suggest that the proposed algorithm has significant implications for energy-efficient strategies in cloud data centers. Generally, this study makes contributes to the development of energy-efficient approaches to VMP that can help reduce power consumption and improve the sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

This paper introduces the d-distanceb-matching problem, in which we are given a bipartite graph G = (S , T ; E) with S = { s 1 , ⋯ , s n } , a weight function on the edges, an integer d ∈ Z + and a degree bound function b : S ∪ T → Z + . The goal is to find a maximum-weight subset M ⊆ E of the edges satisfying the following two conditions: (1) the degree of each node v ∈ S ∪ T is at most b(v) in M, (2) if s i t , s j t ∈ M , then | i - j | ≥ d . In the cyclic version of the problem, the nodes in S are considered to be in cyclic order. We get back the (cyclic)d-distance matching problem when b (s) = 1 for s ∈ S and b (t) = ∞ for t ∈ T . We prove that the d-distance matching problem is APX-hard, even in the unweighted case. We show that 2 - 1 d is a tight upper bound on the integrality gap of the natural integer programming model for the cyclic d-distance b-matching problem provided that (2 d - 1) divides the size of S. For the non-cyclic case, the integrality gap is shown to be at most (2 - 2 d) . The proofs give approximation algorithms with guarantees matching these bounds, and also improve the best known algorithms for the (cyclic) d-distance matching problem. In a related problem, our goal is to find a permutation of S maximizing the weight of the optimal d-distance b-matching. This problem can be solved in polynomial time for the (cyclic) d-distance matching problem — even though the (cyclic) d-distance matching problem itself is NP-hard and also hard to approximate arbitrarily. For (cyclic) d-distance b-matchings, however, we prove that finding the best permutation is NP-hard, even if b ≡ 2 or d = 2 , and we give e-approximation algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we study restricted variations of the following obnoxious facility location problem in the plane: locate k new obnoxious facilities amidst n ordered demand points (existing facility sites) in the plane so that none of the existing facility sites are affected. We study this problem in two restricted settings. The obnoxious facilities are constrained to be centered on (i) a predetermined horizontal line segment pq ¯ , and (ii) the boundary arc of a predetermined disk C . For the problem in (i), we first give an (1 - ϵ) -approximation algorithm that runs in O (n + k) log | | p q | | 2 (k - 1) ϵ time, where ϵ > 0 and ||pq|| is the length of pq ¯ . We then propose two exact polynomial time algorithms, one based on doing binary search on all candidates and the other with the parametric search technique of Megiddo (J ACM 30:852–865, 1983); that will take O (n k) 2 time and O (n + k) 2 time respectively. Continuing further, using the improved parametric technique, we give an O (n log n) -time algorithm for k = 2 . We also show that the (1 - ϵ) -approximation algorithm of (i) can be easily adapted to solve the problem (ii) with an extra multiplicative factor of n in the running time. Finally, we give a O (n 3 k) -time dynamic programming solution to the min-sum obnoxious facility location problem restricted to a line segment where the demand points are associated with weights, and the goal is to minimize the sum of the weights of the points covered. [ABSTRACT FROM AUTHOR]

This paper presents a QR decomposition based low-rank approximation algorithm for training and prediction in Gaussian process regression. Conventional low-rank methods like FITC, Nyström, etc., rely on low-rank approximations to the full kernel matrix that are derived from a set of representative data points. Training with FITC involves the selection of these representative points and is computationally intensive when the dimension of the dataset is large. Prediction accuracy of Nyström suffers when the number of representative points is small or when the length scale is small. The algorithm proposed here uses the thin QR decomposition of the low-rank matrix used in the Nyström approximation. We show that the marginal likelihood and its derivatives needed for training can be split into the subspace belonging to that approximation and its orthogonal complement. During prediction, we further restrict the target vectors into this subspace and thus eliminate the numerical error caused by very low noise variance. Use of the QR factorization improves the training and prediction performance. Experiments on real and synthetic data show that our approach is more accurate and faster than conventional methods. Our algorithm performs well at low length scales and achieves the prediction accuracy comparable to the full kernel matrix approach even for low rank approximations, without the need to optimize the representative points. This results in a simpler and faster approximation that could be used to scale Gaussian process regression to large datasets. [ABSTRACT FROM AUTHOR]

We study the following scheduling problem with transportation. Given a set of n jobs that need to be processed on a single machine, we need to deliver the finished jobs to one of the two destinations using a single transporter. The goal is to minimize the time that all the jobs are delivered to their destination, such that the transporter returns. We propose a 11 / 6 + ε -approximation algorithm which improves upon the best-known approximation ratio of 2. [ABSTRACT FROM AUTHOR]

We consider batch scheduling on m machines to minimize the makespan. Each job has a given set of machines to be assigned. Each machine can process several jobs simultaneously as a batch, and the machines may have different batch capacities. We study two models: (i) scheduling on equal-speed batch machines under a nested processing set restriction, where the machines have the same processing speed, and (ii) scheduling on uniform batch machines under a tree-hierarchical processing set restriction, where the machines have different processing speeds. For both models we design polynomial-time approximation algorithms to solve them. The algorithms have a worst-case ratio of 2 for non-identical batch capacities and a worst-case ratio of 2 - 1 / m for identical batch capacities. [ABSTRACT FROM AUTHOR]

We study a generalization of the well-known traveling salesman problem in a metric space, in which each city is associated with a release time. The salesman has to visit each city at or after its release time. There exists a naive 5/2-approximation algorithm where the salesman simply starts to route the network after all cities are released. Interestingly, this bound has never been improved for more than two decades. In this paper, we revisit the problem and achieve the following results. First, we devise an approximation algorithm with performance ratio less than 5/2 when the number of distinct release times is fixed. Then, we analyze a natural class of algorithms and show that no performance ratio better than 5/2 is possible unless the Metric TSP can be approximated with a ratio strictly less than 3/2, which is a well-known longstanding open question. Finally, we consider a special case where the graph has a heavy edge and present an approximation algorithm with performance ratio less than 5/2. [ABSTRACT FROM AUTHOR]

We study several two-machine shop scheduling problems, namely flow shop, job shop and open shop scheduling problems under linear constraints. In these problems, the processing times of two stages of jobs are also decision variables and satisfy a system of linear constraints. The goal of each problem is to determine the processing time of each job, and to schedule the jobs to the shop machine such that the makespan, i.e., the completion time of all jobs, is minimized. These problems can find application in various areas, such as industrial production, advertising and automotive maintenance. We study the computational complexity and propose polynomial-time optimal or approximation algorithms for them. In particular, we show that although a two-machine flow shop scheduling problem and a two-machine job shop scheduling problem without recirculation can be solved in polynomial time, the problems where processing times satisfy linear constraints are generally NP-hard in the strong sense. Then, we design algorithms for various settings of these problems. We design polynomial-time algorithms for them when there are a fixed number of constraints. For the general case, we first propose a simple 2-approximation algorithm, and then design a polynomial-time approximation schemes. In contrast to the previous two problems, we show that the two-machine open shop scheduling problem under linear constraints can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

Matrix decomposition is a widely used tool in machine learning with many applications such as dimension reduction or visualization. In this paper we consider decomposing X, a matrix of size n × m , to a product WS where we require that S, a matrix of size n × k , needs to have consecutive ones property. More specifically, we require that each row of S needs to be in the form of 0 , ... , 0 , 1 , ... , 1 , 0 , ... , 0 . Such decompositions are particularly meaningful if X is a matrix where each row represents a time series; in such a case the ones in each row in S represent a time segment. We show that the optimization problem is inapproximable. To solve the problem we propose 5 different algorithms. The first two algorithms are based on solving iteratively S while keeping W fixed and then solving W while keeping S fixed. The next two algorithms are based on greedily optimizing a single row in S and the corresponding column in W. The last algorithm first finds the optimal decomposition of with 2 k - 1 non-overlapping rows, and then greedily combines the rows until k rows remain. We compare the algorithms experimentally, focusing on the quality of the decomposition as well as the computational time. We show experimentally that our algorithms yield interpretable results in practical time. [ABSTRACT FROM AUTHOR]

In this paper, we propose and investigate the individually fair k-center with outliers (IFkCO). In the IFkCO, we are given an n-sized vertex set in a metric space, as well as integers k and q. At most k vertices can be selected as the centers and at most q vertices can be selected as the outliers. The centers are selected to serve all the not-an-outlier (i.e., served) vertices. The so-called individual fairness constraint restricts that every served vertex must have a selected center not too far way. More precisely, it is supposed that there exists at least one center among its ⌈ (n - q) / k ⌉ closest neighbors for every served vertex. Because every center serves (n - q) / k vertices on the average. The objective is to select centers and outliers, assign every served vertex to some center, such that the maximum fairness ratio over all served vertices is minimized, where the fairness ratio of a vertex is defined as the ratio between its distance with the assigned center and its distance with a ⌈ (n - q) / k ⌉ th closest neighbor. As our main contribution, a 4-approximation algorithm is presented, based on which we develop an improved algorithm from a practical perspective. Extensive experiment results on both synthetic datasets and real-world datasets are presented to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

We consider the max hypergraph 3-cut problem with limited unbalance (MH3C-LU). The objective is to divide the vertex set of an edge-weighted hypergraph H = (V , E , w) into three disjoint subsets V 1 , V 2 , and V 3 such that the sum of edge weights cross different parts is maximized subject to | | V i | - | V l | | ≤ B ( ∀ i ≠ l ∈ { 1 , 2 , 3 } ) for a given parameter B. This problem is NP-hard because it includes some well-known problems like the max 3-section problem and the max 3-cut problem as special cases. We formulate the MH3C-LU as a ternary quadratic program and present a randomized approximation algorithm based on the complex semidefinite programming relaxation technique. [ABSTRACT FROM AUTHOR]

This paper presents a combined method for the equivalent circuit parameters estimation of cage induction motors. The method is based on dual usage of the particle swarm optimization algorithm and the approximation of rotor parameters as a function of the speed, due to the influence of the skin effect. The approximation of rotor parameters as a function of the speed is not directly applied in the parameters estimation algorithm, but it is used to obtain the torque-speed characteristics of cage induction motors. The first stage of the optimization includes the estimation of equivalent circuit parameters of a motor for the nominal operating mode, while the rotor parameters at the start of the motor are estimated in the second stage of the optimization. These parameters are obtained as a result of minimizing the error between the calculated and the manufacture data. The proposed method is applied on eight two-pole induction motors with different rated powers, energy efficiency class IE3, manufactured by ABB. The results are verified by comparing the obtained torque-speed characteristics with the corresponding characteristics provided by the manufacturer (i.e., ABB) using the MotSize program. It is shown that the torque-speed characteristics obtained by the proposed method are in a good agreement with the characteristics given by the manufacturer. Also, the value of the mean absolute relative error does not exceed 5% for all considered motors. [ABSTRACT FROM AUTHOR]

Constrained partially observable Markov decision processes (CPOMDPs) have been used to model various real-world phenomena. However, they are notoriously difficult to solve to optimality, and there exist only a few approximation methods for obtaining high-quality solutions. In this study, grid-based approximations are used in combination with linear programming (LP) models to generate approximate policies for CPOMDPs. A detailed numerical study is conducted with six CPOMDP problem instances considering both their finite and infinite horizon formulations. The quality of approximation algorithms for solving unconstrained POMDP problems is established through a comparative analysis with exact solution methods. Then, the performance of the LP-based CPOMDP solution approaches for varying budget levels is evaluated. Finally, the flexibility of LP-based approaches is demonstrated by applying deterministic policy constraints, and a detailed investigation into their impact on rewards and CPU run time is provided. For most of the finite horizon problems, deterministic policy constraints are found to have little impact on expected reward, but they introduce a significant increase to CPU run time. For infinite horizon problems, the reverse is observed: deterministic policies tend to yield lower expected total rewards than their stochastic counterparts, but the impact of deterministic constraints on CPU run time is negligible in this case. Overall, these results demonstrate that LP models can effectively generate approximate policies for both finite and infinite horizon problems while providing the flexibility to incorporate various additional constraints into the underlying model. [ABSTRACT FROM AUTHOR]

In malleable job scheduling, jobs can be executed simultaneously on multiple machines with the processing time depending on the number of allocated machines. In this setting, jobs are required to be executed non-preemptively and in unison, in the sense that they occupy, during their execution, the same time interval over all the machines of the allocated set. In this work, we study generalizations of malleable job scheduling inspired by standard scheduling on unrelated machines. Specifically, we introduce a general model of malleable job scheduling, where each machine has a (possibly different) speed for each job, and the processing time of a job j on a set of allocated machines S depends on the total speed of S with respect to j. For machines with unrelated speeds, we show that the optimal makespan cannot be approximated within a factor less than e e - 1 , unless P = N P . On the positive side, we present polynomial-time algorithms with approximation ratios 2 e e - 1 for machines with unrelated speeds, 3 for machines with uniform speeds, and 7/3 for restricted assignments on identical machines. Our algorithms are based on deterministic LP rounding. They result in sparse schedules, in the sense that each machine shares at most one job with other machines. We also prove lower bounds on the integrality gap of 1 + φ for unrelated speeds (φ is the golden ratio) and 2 for uniform speeds and restricted assignments. To indicate the generality of our approach, we show that it also yields constant factor approximation algorithms for a variant where we determine the effective speed of a set of allocated machines based on the L p norm of their speeds. [ABSTRACT FROM AUTHOR]

In this paper we consider the coupled task scheduling problem with exact delay times on a single machine with the objective of minimizing the total completion time of the jobs. We provide constant-factor approximation algorithms for several variants of this problem that are known to be N P -hard, while also proving N P -hardness for two variants whose complexity was unknown before. Using these results, together with constant-factor approximations for the makespan objective from the literature, we also introduce the first results on bi-objective approximation in the coupled task setting. [ABSTRACT FROM AUTHOR]

Voronoi diagrams on triangulated surfaces based on the geodesic metric play a key role in many applications of computer graphics. Previous methods of constructing such Voronoi diagrams generally depended on having an exact geodesic metric. However, exact geodesic computation is time-consuming and has high memory usage, limiting wider application of geodesic Voronoi diagrams (GVDs). In order to overcome this issue, instead of using exact methods, we reformulate a graph method based on Steiner point insertion, as an effective way to obtain geodesic distances. Further, since a bisector comprises hyperbolic and line segments, we utilize Apollonius diagrams to encode complicated structures, enabling Voronoi diagrams to encode a medial-axis surface for a dense set of boundary samples. Based on these strategies, we present an approximation algorithm for efficient Voronoi diagram construction on triangulated surfaces. We also suggest a measure for evaluating similarity of our results to the exact GVD. Although our GVD results are constructed using approximate geodesic distances, we can get GVD results similar to exact results by inserting Steiner points on triangle edges. Experimental results on many 3D models indicate the improved speed and memory requirements compared to previous leading methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose some new semidefinite relaxations for a class of nonconvex complex quadratic programming problems, which widely appear in the areas of signal processing and power system. By deriving new valid constraints to the matrix variables in the lifted space, we derive some enhanced semidefinite relaxations of the complex quadratic programming problems. Then, we compare the proposed semidefinite relaxations with existing ones, and show that the newly proposed semidefinite relaxations could be strictly tighter than the previous ones. Moreover, the proposed semidefinite relaxations can be applied to more general cases of complex quadratic programming problems, whereas the previous ones are only designed for special cases. Numerical results indicate that the proposed semidefinite relaxations not only provide tighter relaxation bounds, but also improve some existing approximation algorithms by finding better sub-optimal solutions. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), the virtual backbone (VB) consists of a subset of nodes, which are responsible for routing tasks. Fault-tolerant VBs are desirable for overcoming the effects of node or link failure in WSNs. Usually, a homogeneous WSN (VB) is abstracted as a unit disk graph (UDG) (connected dominating set(CDS)). This paper introduces the concept of a fault-tolerant CDS in a UDG called a ((2, 2), m)-CDS, which is different from a traditional fault-tolerant CDS ((k, m)-CDS). A ((2, 2), m)-CDS can still function even if one edge or one node fails, which implies that it possesses fault-tolerant properties for both nodes and edges, in contrast to traditional (k, m)-CDSs, which possess fault tolerance only for nodes. Then, we propose a 5 α -approximation algorithm for computing a ((2, 2), m)-CDS, where α is the performance ratio for computing a (2, m) -CDS, and analyze its time complexity. In simulations, we compare our algorithm with an existing algorithm for fault-tolerant CDS construction based on CDS size. From the simulations, we find that our algorithm outperforms its competitor in constructing a quality VB. [ABSTRACT FROM AUTHOR]

We consider scheduling monotonic moldable tasks on multiple platforms, where each platform contains a set of processors. A moldable task can be split into several pieces of equal size and processed simultaneously on multiple processors. Tasks are not allowed to be processed spanning over platforms. This scheduling model has many applications, ranging from parallel computing to the berth and quay crane allocation and the workforce assignment problem. We develop several approximation algorithms aiming at minimizing the makespan. More precisely, we provide a 2-approximation algorithm for identical platforms, a Fully Polynomial Time Approximation Scheme (FPATS) under the assumption of large processor counts and a 2-approximation algorithm for a fixed number of heterogeneous platforms. Most of the proposed algorithms combine a dual approximation scheme with a novel approach to improve the dual approximation algorithm. All results can be extended to the contiguous case, i.e., a task can only be executed by contiguously numbered processors. [ABSTRACT FROM AUTHOR]

We consider the intractable problem of finding several edge-disjoint spanning trees of the minimum total weight with a given diameter in complete undirected graph in current paper. The weights of edges of a graph are random variables from several continuous distributions: uniform, biased truncated exponential, biased truncated normal. The approximation algorithm with time complexity (n2), where n is number of vertices in graph, is proposed for solving this problem. The asymptotic optimality conditions for constructed algorithm is presented for each considered probabilistic distribution. [ABSTRACT FROM AUTHOR]

This paper presents the distributed primal outer approximation (DiPOA) algorithm for solving sparse convex programming (SCP) problems with separable structures, efficiently, and in a decentralized manner. The DiPOA algorithm development consists of embedding the recently proposed relaxed hybrid alternating direction method of multipliers (RH-ADMM) algorithm into the outer approximation (OA) algorithm. We also propose two main improvements to control the quality and the number of cutting planes that approximate nonlinear functions. In particular, the RH-ADMM algorithm acts as a distributed numerical engine inside the DiPOA algorithm. DiPOA takes advantage of the multi-core architecture of modern processors to speed up optimization algorithms. The proposed distributed algorithm makes practical the solution of SCP in learning and control problems from the application side. This paper concludes with a performance analysis of DiPOA for the distributed sparse logistic regression and quadratically constrained optimization problems. Finally, the paper concludes with a numerical comparison with state-of-the-art optimization solvers. [ABSTRACT FROM AUTHOR]

The problem of designing a robustified Kalman filtering technique, insensitive to spiky observations, or outliers, contaminating the Gaussian observations has been presented in the paper. Firstly, a class of M-robustified dynamic stochastic approximation algorithms is derived by minimizing at each stage a specific time-varying M-robust performance index, that is, general for a family of algorithms to be considered. The gain matrix of a particular algorithm is calculated at each stage by minimizing an additional criterion of the approximate minimum variance type, with the aid of the statistical linearization method. By combining the proposed M-robust estimator with the one-stage optimal prediction, in the minimum mean-square error sense, a new statistically linearized M-robustified Kalman filtering technique has been derived. Two simple practical versions of the proposed M-robustified state estimator are derived by approximating the mean-square optimal statistical linearization coefficient with the fixed and the time-varying factors. The feasibility of the approaches has been analysed by the simulations, using a manoeuvring target radar tracking example, and the real data, related to an object video tracking using short-wave infrared camera. [ABSTRACT FROM AUTHOR]

In recent years, order-reduction techniques based on artificial intelligence algorithms have become a topic of interest in the structural dynamics community. In this paper, we describe a new artificial intelligence technique based on the artificial neural network used to reduce a large interval system. Applied to reduce the degree of the polynomial numerator and denominator each separately, by allowing them to learn automatically from the original system, this machine learning phase allows the algorithm to improve over time and control performance of the approximation, maintaining as much as possible the stability of the dynamic system, and reducing errors between the original system and the reduced system that are presented as a very acceptable approximation, a comparison study is presented between existing works and the proposed technique, with the help of examples from literature. [ABSTRACT FROM AUTHOR]

The quadratically constrained quadratic programming problem often appears in various fields such as engineering practice, management science and network communication. This paper mainly considers a non-convex quadratic programming problem with convex quadratic constraints. Firstly, the objective function of the problem is reconstructed into a form composed of only one convex function and several linear functions by using the eigenvalue decomposition technique of matrix. Then the reconstructed problem is converted to the equivalent problem with simple concave quadratic objective function in the outcome space by introducing appropriate auxiliary variables, and its feasible domain is convex. Based on the branch-and-bound framework which can guarantee the global optimality of the solution, a global optimization algorithm for solving the equivalent problem is proposed, which integrates the effective relaxation process and the branching process related to the outer approximation technique. Finally, the effectiveness and feasibility of the algorithm are illustrated by numerical experiments. [ABSTRACT FROM AUTHOR]

We study the weak convergence of a greedy algorithm of approximation by a given set in a Banach space. It is proved that the greedy algorithm of approximation by a strongly norm-reducing set in a uniformly smooth Banach space with the WN-property weakly converges. In an arbitrary separable Banach space without the WN-property, we construct an example of a strongly norm-reducing set such that the greedy algorithm of approximation by this set does not weakly converge for some initial element. [ABSTRACT FROM AUTHOR]

The five-axis flank milling process of sculptured surfaces is an intricate process involving time-varying and nonlinear characteristics under the multiphysics coupling effect. The milling force during the process is an important factor that influences the machining accuracy of sculptured surfaces. Face gears, as parts with sculptured surfaces, are widely employed in the aerospace and machinery manufacturing industries. To realize the high-precision manufacturing of face gears with a universal milling machine, this study proposes a five-axis flank milling method for face gears. In addition, based on the tangent plane approximation algorithm and the Receptance Coupling Substructure Analysis (RCSA) method, the milling force and the tool tip vibration during the face gear machining process are investigated. Finally, a five-axis flank milling force model and a face gear surface machining error model are established considering the interaction between the milling force and the process system. [ABSTRACT FROM AUTHOR]

The single facility location problem in multiple regions with different norms (SMDN) generalizes the well-known Weber Problem. The SMDN consists of finding the optimum location of a single facility in the plane which is partitioned into multiple regions where the distance to travel in each region is measured or approximated with different norms. In this study, we specifically focus on the SMDN considering three regions with either rectilinear or Euclidean norms. We first introduce some analytical properties of this problem. Then, we devise a specially tailored branch-and-bound algorithm, i.e. a Big Square Small Square algorithm (BSSS), and two heuristics, named as Discrete Approximation algorithm (DA) and Modified Weiszfeld procedure (MW). The performance of the proposed approaches are tested using both standard test instances from the literature and randomly generated instances. According to our extensive computational experiments, the BSSS stands out to be a suitable exact solution approach in terms of both accuracy and efficiency when commercial mixed integer nonlinear solvers are not applicable. Besides, we also observe that the DA yields quite accurate solutions at the expense of high computation times while the MW arises to be the most efficient method with the least accuracy. [ABSTRACT FROM AUTHOR]

The Unsplittable Flow on a Path (UFP) problem has garnered considerable attention as a challenging combinatorial optimization problem with notable practical implications. Steered by its pivotal applications in power engineering, the present work formulates a novel generalization of UFP, wherein demands and capacities in the input instance are monotone step functions over the set of edges. As an initial step towards tackling this generalization, we draw on and extend ideas from prior research to devise a quasi-polynomial time approximation scheme under the premise that the demands and capacities lie in a quasi-polynomial range. Second, retaining the same assumption, an efficient logarithmic approximation is introduced for the single-source variant of the problem. Finally, we round up the contributions by designing a (kind of) black-box reduction that, under some mild conditions, allows to translate LP-based approximation algorithms for the studied problem into their counterparts for the Alternating Current Optimal Power Flow problem—a fundamental workflow in operation and control of power systems. [ABSTRACT FROM AUTHOR]

Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model interpretability deteriorates. Traditional tree-induction algorithms, such as C4.5 and CART, rely on impurity-reduction functions that promote the discriminative power of each split. Thus, although these traditional methods are accurate in practice, there has been no theoretical guarantee that they will produce small trees. In this paper, we justify the use of a general family of impurity functions, including the popular functions of entropy and Gini-index, in scenarios where small trees are desirable, by showing that a simple enhancement can equip them with complexity guarantees. We consider a general setting, where objects to be classified are drawn from an arbitrary probability distribution, classification can be binary or multi-class, and splitting tests are associated with non-uniform costs. As a measure of tree complexity, we adopt the expected cost to classify an object drawn from the input distribution, which, in the uniform-cost case, is the expected number of tests. We propose a tree-induction algorithm that gives a logarithmic approximation guarantee on the tree complexity. This approximation factor is tight up to a constant factor under mild assumptions. The algorithm recursively selects a test that maximizes a greedy criterion defined as a weighted sum of three components. The first two components encourage the selection of tests that improve the balance and the cost-efficiency of the tree, respectively, while the third impurity-reduction component encourages the selection of more discriminative tests. As shown in our empirical evaluation, compared to the original heuristics, the enhanced algorithms strike an excellent balance between predictive accuracy and tree complexity. [ABSTRACT FROM AUTHOR]

We consider a knapsack-constrained maximization problem of a nonnegative monotone DR-submodular function f over a bounded integer lattice [ B ] in R + n , max { f (x) : x ∈ [ B ] and ∑ i = 1 n x (i) c (i) ≤ 1 } , where n is the cardinality of a ground set N and c (·) is a cost function defined on N. Soma and Yoshida [Math. Program., 172 (2018), pp. 539-563] present a (1 - e - 1 - O (ϵ)) -approximation algorithm for this problem by combining threshold greedy algorithm with partial element enumeration technique. Although the approximation ratio is almost tight, their algorithm runs in O (n 3 ϵ 3 log 3 τ [ log 3 B ∞ + n ϵ log B ∞ log 1 ϵ c min ]) time, where c min = min i c (i) and τ is the ratio of the maximum value of f to the minimum nonzero increase in the value of f. Besides, Ene and Nguy e ˇ ~ n [arXiv:1606.08362, 2016] indirectly give a (1 - e - 1 - O (ϵ)) -approximation algorithm with O ((1 ϵ) O (1 / ϵ 4) n log ‖ B ‖ ∞ log 2 (n log ‖ B ‖ ∞)) time. But their algorithm is random. In this paper, we make full use of the DR-submodularity over a bounded integer lattice, carry forward the greedy idea in the continuous process and provide a simple deterministic rounding method so as to obtain a feasible solution of the original problem without loss of objective value. We present a deterministic algorithm and theoretically reduce its running time to a new record, O ( (1 ϵ) O (1 / ϵ 5) · n log 1 c min log ‖ B ‖ ∞) , with the same approximate ratio. [ABSTRACT FROM AUTHOR]

Existing methods of stress intensity factor (SIF) estimation are commonly based on the contact measurements of crack length with subsequent computation at taking into account the specimen geometry and loading process. The paper aims at the enhancement and verification of the non-contact and easily implemented technique for the assessment of mode I SIF during mechanical tests. Displacement fields constructed by digital image correlation (DIC) were approximated using Williams series in order to determine precise crack tip coordinates and proceed to the computation of stress intensity factor (SIF). The approximation algorithm was tested on linear-elastic and elastic–plastic ABAQUS models under various stress levels and the different Williams series term number. Besides numerical modeling of SIF, the algorithm was tested through assessment of crack propagation parameters under fatigue crack growth with the use of coarse- and fine-grained titanium samples. For the crack length range a/W ≤ 0.3, the approximation algorithm using the Williams series may be employed for estimating crack tip coordinates (crack length) and mode I stress intensity factor with satisfactory level of accuracy. The results of the study appeared to hold the promise of automated non-contact measurements of the crack length as well as stress intensity factor under fatigue crack propagation by employing DIC and appropriate high-performance real-time computing. [ABSTRACT FROM AUTHOR]

Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with non-dominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance. To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms. [ABSTRACT FROM AUTHOR]