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Cuk converter is a fourth order, non-linear DC-DC converter. It is a combination of both buck and boost converters with inverting topology where the output may be higher or lower with regard to the input. This paper presents diminution of Cuk converter using two different techniques, namely factor division algorithm to obtain numerator coefficients and Routh approximation method to obtain denominator coefficients which simplify the model to reduce simulation time and cost as compared to original system. For the original system and its reduced order systems, the outcomes are assessed using step and Bode responses. Additionally, to analyze the effectiveness of the developed model, the integral absolute error and the integral of the time-weighted absolute error are calculated. [ABSTRACT FROM AUTHOR]

In this article we design efficient neural network based on new BFGS training algorithm. In each iteration, the updated of Hessian approximations traditionally algorithm required high memory, storage and computational overhead. Herein we suggest a new update. One of these update innovative features is its ability to estimate the objective function's (energy functions) 2nd order curvature with high order precision while utilizing the given gradient and function value data. Under some presumptions, the approach is demonstrated to be globally convergent. The updated BFGS techniques can be numerical and more efficient than the existing comparable BFGS methods, as shown by numerical trials. Then we used suggested training algorithm to learn suitable neural network for solving ZK-BBM equation with high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model, a numerical algorithm for the process of transfer and diffusion of harmful substances, is developed, which takes into account the wind speed in three directions and the rate of deposition of aerosol particles on the underlying surface, as well as the capture of particles by vegetation elements, which play a significant role in the dynamics of the object of study. In order to integrate the task, an implicit finite-difference scheme and an algorithm with the second order of approximation in terms of time and space variables were developed with the help of which numerical calculations can be carried out on a computer system, on the basis of which it is possible to investigate and predict the ecological state of the industrial region under consideration. To investigate the adequacy of the developed mathematical apparatus, computational experiments were carried out on a computer, the results of which were compared with full-scale data of a real production facility located in the Tashkent region of Uzbekistan. [ABSTRACT FROM AUTHOR]

The main aim of the paper is to suggest an algorithm to solve approximately the initial value problem for scalar non-linear fractional differential equation with generalized proportional fractional derivative on a finite interval. The main condition is connected with the one side Lipschitz condition of the right hand sid-e part of the given equation. An iterative scheme, based on appropriately defined mild lower and mild upper solutions, is provided. An algorithm for successive approximations is well ground. Two monotone sequences, increasing and decreasing ones, are constructed. Their convergence to the mixed mild solution of the given problem is proved. In the case of uniqueness, both limits coincide the unique solution of the given problem. The approximate method is based on the application of the method of lower and upper solutions combined with monotone-iterative technique. [ABSTRACT FROM AUTHOR]

Function approximation is one of basic subtasks in engineering design. Current study is focused on development and adaption of function approximation algorithms based on higher order wavelet method. Two case studies are implemented in order to validate the proposed function approximation models. The theoretical study is validated by numerical analysis. The accuracy and the numerical rate of convergence with respect to mesh, also the complexity of the implementation depend on generalized wavelet model parameter value utilized. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm for computing the approximation of the potential of an infinitely thin disk on a Cartesian grid. Results computed for the potential of a disk with a specified surface density are reported. [ABSTRACT FROM AUTHOR]

The differential equation for anisotropic plate bending is not always solvable analytically due to mathematical difficulties. Local loads, and the conditions for securing the plate's edges have a significant impact. Analytical processes based on numbers are often utilized, but there is no universal methodology. This study aimed to investigate the bendable anisotropic plates using the numerical-analytic boundary element method. Even though the numerical-analytic boundary element approach was only established recently, it has already proven beneficial in tackling various issues [15, 16]. The method allows constructing a central system of solutions for the differential equations of isotropic and orthotropic plate bending [15, 16] without limitation on load properties or fixing circumstances. The Kantorovich-Vlasov variation technique reduces two-dimensional to a one-dimensional problem. It is recommended that a dynamic or static function of the lateral deflection distribution be selected. In general form, characteristic equations and fundamental functions are given. As a result, the problem of bending an anisotropic plate managed to four different root combinations of the equation's characteristic, implying that 64 primary function analytical expressions will give the total solution. The numerical-analytic approximation algorithm for boundary elements [15, 16, 18, 19] can be used to determine all principal functions, generate Green's functions, and so on after solving the Cauchy problem to determine constants. The proposed method solves the problem of anisotropic rectangular plate bending under homogeneous and inhomogeneous constraints, regardless of the load applied. [ABSTRACT FROM AUTHOR]

Engineering educators use MATLAB in multiple disciplines. The new features of MATLAB, such as MATLAB Grader and live scripts, can help students gain not only skills of analysis and design with MATLAB as a tool but also a better understanding of engineering concepts. MATLAB programming exercises are required in homework in a Vibrations course and computer lab projects in a Signals and Systems course. Automated grading with MATLAB Grader provides immediate feedback, which allows students to learn from their own mistakes while they try to pass the multiple requirements for each exercise that the instructor configured in advance. MATLAB live scripts are used in the teaching of Signals and Systems. Students are provided with lab manuals written in live scripts, allowing the integration of formatted text, equations, images, and MATLAB code in a single environment. With the live scripts, the concepts related to the MATLAB programming exercises are reinforced without the need of referring to another document or PowerPoint slides, and the examples embedded in the document can be run section by section with results displayed below the code or in an output window next to the document. Interactive components, such as a slider bar to change the value of time t in the convolution integral procedure and animation of the approximation of a signal using Fourier Series, are included in the examples to further improve the student learning experience. The instructor also used MATLAB live scripts during their lectures. At the end of the semester, the students completed a survey about their experiences with MATLAB Grader and live scripts. Students found these tools helpful in learning Vibrations and Signals & Systems. Setting up the auto-grading system was more effort in one semester than grading the work but will result in a reduced professor workload in future semesters. [ABSTRACT FROM AUTHOR]

We consider a model for repeated stochastic matching where compatibility is probabilistic, is realized the first time agents are matched, and persists in the future. Such a model has applications in the gig economy, kidney exchange, and mentorship matching. We ask whether a decentralized matching process can approximate the optimal online algorithm. In particular, we consider a decentralized stable matching process where agents match with the most compatible partner who does not prefer matching with someone else, and known compatible pairs continue matching in all future rounds. We demonstrate that the above process provides a 0.316-approximation to the optimal online algorithm for matching on general graphs. We also provide a 1/7-approximation for many-to-one bipartite matching, a 1/11-approximation for capacitated matching on general graphs, and a 1/2k-approximation for forming teams of up to k agents. Our results rely on a novel coupling argument that decomposes the successful edges of the optimal online algorithm in terms of their round-by-round comparison with stable matching. [ABSTRACT FROM AUTHOR]

We consider the algorithmic question of choosing a subset of candidates of a given size k from a set of m candidates, with knowledge of voters' ordinal rankings over all candidates. We consider the well-known and classic scoring rule for achieving diverse representation: the Chamberlin-Courant (CC) or 1-Borda rule, where the score of a committee is the average over the voters, of the rank of the best candidate in the committee for that voter; and its generalization to the average of the top s best candidates, called the s-Borda rule. Our first result is an improved analysis of the natural and well-studied greedy heuristic. We show that greedy achieves a (1 - 2 / k+1 )-approximation to the maximization (or satisfaction) version of CC rule, and a (1 - 2s / k+1 )-approximation to the s-Borda score. This significantly improves the existing submodularity-based analysis of the greedy algorithm that only shows a (1 - 1/e)-approximation. Our result also improves on the best known approximation algorithm for this problem. We achieve this result by showing that the average dissatisfaction score for the greedy algorithm is at most 2m+1/k+1 for the CC rule, and at most 2s2m+1/k+1 for s-Borda. We show these dissatisfaction score bounds are tight up to constants, and even the constant factor of 2 in the case of the CC rule is almost tight. For the dissatisfaction (or minimization) version of the problem, it is known that the average dissatisfaction score of the best committee cannot be approximated in polynomial time to within any constant factor when s is a constant (under standard computational complexity assumptions). As our next result, we strengthen this to show that the score of m+1/k+1 can be viewed as an optimal benchmark for the CC rule, in the sense that it is essentially the best achievable score of any polynomial-time algorithm even when the optimal score is a polynomial factor smaller. We show that another well-studied algorithm for this problem, called the Banzhaf rule, attains this benchmark. We finally show that for the s-Borda rule, when the optimal value is small, these algorithms can be improved by a factor of Ω (√s) via LP rounding. Our upper and lower bounds are a significant improvement over previous results, and taken together, not only enable us to perform a finer comparison of greedy algorithms for these problems, but also provide analytic justification for using such algorithms in practice. [ABSTRACT FROM AUTHOR]

The solution of environmental monitoring problems is often limited to the use of proprietary software, which can adversely affect costs, flexibility, and data integration. The need for environmental monitoring system is especially acute in the field of public environmental monitoring, where teams of potential users unite researchers from different areas of science from biology and chemistry to sociology and economics. Simulation technology of environmental monitoring involves three main areas of activity: observations of impact factors and environmental conditions; assessment of the current state of the environment; forecasting the state of the natural environment and assessing the projected condition. Simulation technology of environmental monitoring includes approximation methods as estimation tools. To investigate the areas that are affected by the rocket stages conducted, this article suggests the creation of demonstration zones. Such zones are characterized by numerous sources and high level of pollutants penetrating into the ground. This paper describes the adaptive library of approximation algorithms to assess the concentration of unburned UDMH in ground to the extent that this concentration was verified in the study and in samples from specific sites from the fall regions of expendable stages of the rocket. Thus, the adaptive library makes it possible to obtain a preliminary estimate of the concentration of an environmentally hazardous substance, which can subsequently form the basis for possible regulatory actions. A problem of the environmental impacts of rocket fuel components is used to demonstrate the implementation of adaptive library. To analyze the concentration of unburned UDMH in ground, it is proposed to use the data of expedition’s ground samples from different fall regions at different times and under different weather conditions. We further show how several results illustrate the usage of proposed adaptive library. [ABSTRACT FROM AUTHOR]

Previous studies show that parameter value for car following model, such as, maximum and minimum headway, acceleration, and gap are necessary but there is a big error found while comparing between original and simulated data. Therefore, it is not appropriately applicable to determine the real driver behavior for the different traffic systems. In this study, the calibration method has been proposed to estimate the model parameter and to find the best fit to the car following model. Here, Intelligent Driver Model (IDM) has been used for simulating car behavior and data have been collected from NGSIM eastbound I-80 in the San Francisco Bay area in Emeryville, CA, USA. Simultaneous perturbation stochastic approximation algorithm (SPSAA) has been used as the calibration model in the system to calculate parameters. The calculation has been made by using MATLAB software. SPSAA is proven to have a very much lower error between original simulation data and real trajectory observation I-80 data. Furthermore, this paper investigates that such kinds of estimated parameter are more appropriate to be used in different traffic system. [ABSTRACT FROM AUTHOR]

We propose two solution concepts for matchings under preferences: robustness and near stability. The former strengthens while the latter relaxes the classic definition of stability by Gale and Shapley (1962). Informally speaking, robustness requires that a matching must be stable in the classic sense, even if the agents slightly change their preferences. Near stability, on the other hand, imposes that a matching must become stable (again, in the classic sense) provided the agents are willing to adjust their preferences a bit. Both of our concepts are quantitative; together they provide means for a fine-grained analysis of the stability of matchings. Moreover, our concepts allow the exploration of trade-offs between stability and other criteria of social optimality, such as the egalitarian cost and the number of unmatched agents. We investigate the computational complexity of finding matchings that implement certain predefined trade-offs. We provide a polynomial-time algorithm that, given agent preferences, returns a socially optimal robust matching, and we prove that finding a socially optimal and nearly stable matching is computationally hard. [ABSTRACT FROM AUTHOR]

In this work we derive efficient rational L∞ approximations of various degrees for the quadruple precision computation of the matrix exponential. We focus especially on the two classes of normal and nonnegative matrices. Our method relies on Remez algorithm for rational approximation while the innovation here is the choice of the starting set of non-symmetrical Chebyshev points. Only one Remez iteration is then usually enough to quickly approach the actual L∞ approximant [ABSTRACT FROM AUTHOR]

An inductive algorithm is presented for smooth approximation of functions based on the Tikhonov regularization method. The discrepancy principle is used for estimating the optimal value of the smoothing parameter. The principle of heuristic self-organization is applied for assessment of some parameters and the optimal complexity of the approximating function. In this paper the efficiency of this algorithm was investigated for different kind of the Tikhonov parametric functional using model functions distorted by additive Gaussian noise simulated by a random numbers generator. [ABSTRACT FROM AUTHOR]

In this paper we propose a simple and cheap method for extracting inner polytopes, i.e., entirely feasible convex regions in which all points satisfy the constraints. The method performs an inner linearization of a set of nonlinear constraints by using a Taylor form. Unlike a previous proposal, the expansion point of the Taylor form is not limited to the bounds of the domains; it can be given by any point inside the studied region producing, in general, a tighter approximation. The approach was used as an upper bounding method in a state-of-the-art global branch & bound optimizer. In the studied instances, the new method finds in average much more inner regions (in 20% of the processed nodes) than the original approach (in 5% of the nodes). [ABSTRACT FROM AUTHOR]

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We introduce a new class of anticipative backward stochastic differential equations with a dependence of McKean type on the law of the solution, that we name MKABSDE. We provide existence and uniqueness results in a general framework with relatively general regularity assumptions on the coefficients. We show how such stochastic equations arise within the modern paradigm of derivative pricing where a central counterparty (CCP) requires the members to deposit variation and initial margins to cover their exposure. In the case when the initial margin is proportional to the Conditional Value-at-Risk (CVaR) of the contract price, we apply our general result to define the price as a solution of a MKABSDE. We provide several linear and non-linear simpler approximations, which we solve using different numerical (deterministic and Monte-Carlo) methods. [ABSTRACT FROM AUTHOR]

Approximation technologies have regained much interest in recent years in the context of wide variety of computation techniques and large scale optimization. The purpose of this study is to outline the conceptual framework and architectural design of adaptive library for approximate solutions based on multi-extremum functions. Simulation technology of complex systems includes approximation methods as estimation tools. Despite the fast development of computing power and technology, many problems of multi-extremum optimization are known to be computationally difficult. In this study we describe the adaptation library of algorithms and programs provides the realization of surrogate based modeling technique using hybrid approximation method, in which the goal is to implement sets of special functions differing in possibility of selecting special types of functions in conjunction with fast greedy algorithms or genetic optimization parameters. The adaptation library includes hybrid approximation algorithms, special models, data structures and several complete solutions for simulation various classes of approximation problems. Adaptation to these problems is based on three selection methods of calculation process: procedural, parametric and combination method. More than two hundreds approximate models and algorithms are integrated in this special models library. A problem of the environmental factors impacts on complex systems is used to demonstrate the implementation of adaptive library. We further show how several fastrate results illustrate the usage of hybrid approximate algorithms. Furthermore, comparative analysis of hybrid algorithms has presented robustness of proposed adaptive library. [ABSTRACT FROM AUTHOR]

Mobile and IoT scenarios increasingly involve interactive and computation intensive contextual recognition. Existing optimizations typically resort to computation offloading or simplified on-device processing. Instead, we observe that the same application is often invoked on multiple devices in close proximity. Moreover, the application instances often processsimilar contextual data that map to thesame outcome. In this paper, we proposecross-device approximate computation reuse, which minimizes redundant computation by harnessing the "equivalence'' between different input values and reusing previously computed outputs with high confidence. We devise adaptive locality sensitive hashing (A-LSH) and homogenized k nearest neighbors (H-kNN). The former achieves scalable and constant lookup, while the latter provides high-quality reuse and tunable accuracy guarantee. We further incorporate approximate reuse as a service, called \name, in the computation offloading runtime. Extensive evaluation shows that, when given 95% accuracy target, \name\ consistently harnesses over 90% of reuse opportunities, which translates to reduced computation latency and energy consumption by a factor of 3 to 10. [ABSTRACT FROM AUTHOR]

We study the effects of the phonon-phonon coupling on the low-energy electric dipole response within a microscopic model based on an effective Skyrme interaction. The finite rank separable approach for the quasiparticle random phase approximation is used. Choosing as an example the isotopic chain of Calcium, we show the ability of the method to describe the low-energy E1 strength distribution. With one and the same set of parameters we describe available experimental data for 48Ca and predict the electric dipole strength function for 50Ca. [ABSTRACT FROM AUTHOR]

We study the problem of allocating a set of indivisible goods among a set of agents in a fair and efficient manner. An allocation is said to be fair if it is envy-free up to one good (EF1), which means that each agent prefers its own bundle over the bundle of any other agent up to the removal of one good. In addition, an allocation is deemed efficient if it satisfies Pareto efficiency. While each of these well-studied properties is easy to achieve separately, achieving them together is far from obvious. Recently, Caragiannis et al. (2016) established the surprising result that when agents have additive valuations for the goods, there always exists an allocation that simultaneously satisfies these two seemingly incompatible properties. Specifically, they showed that an allocation that maximizes the Nash social welfare objective is both EF1 and Pareto efficient. However, the problem of maximizing Nash social welfare is NP-hard. As a result, this approach does not provide an efficient algorithm for finding a fair and efficient allocation. In this paper, we bypass this barrier, and develop a pseudopolynomial time algorithm for finding allocations that are EF1 and Pareto efficient; in particular, when the valuations are bounded, our algorithm finds such an allocation in polynomial time. Furthermore, we establish a stronger existence result compared to Caragiannis et al. (2016): For additive valuations, there always exists an allocation that is EF1 and fractionally Pareto efficient. Another key contribution of our work is to show that our algorithm provides a polynomial-time 1.45-approximation to the Nash social welfare objective. This improves upon the best known approximation ratio for this problem (namely, the 2-approximation algorithm of Cole et al., 2017), and also matches the lower bound on the integrality gap of the convex program of Cole et al. (2017). Unlike many of the existing approaches, our algorithm is completely combinatorial, and relies on constructing integral Fisher markets wherein specific equilibria are not only efficient, but also fair. [ABSTRACT FROM AUTHOR]

We describe the possibility of employing the special case of the 3-SAT problem stemming from the well known integer factorization problem for the quantum cryptography. It is known, that for every instance of our 3-SAT setting the given 3-CNF is satisfiable by a unique truth assignment, and the goal is to find this assignment. Since the complexity status of the factorization problem is still undefined, development of approximation algorithms and heuristics adopts interest of numerous researchers. One of promising approaches to construction of approximation techniques is based on real-valued relaxation of the given 3-CNF followed by minimizing of the appropriate differentiable loss function, and subsequent rounding of the fractional minimizer obtained. Actually, algorithms developed this way differ by the rounding scheme applied on their final stage. We propose a new rounding scheme based on Bayesian learning. The article shows that the proposed method can be used to determine the security in quantum key distribution systems. In the quantum distribution the Shannon rules is applied and the factorization problem is paramount when decrypting secret keys. [ABSTRACT FROM AUTHOR]

We present a branch and bound algorithm for the maximum clique problem in arbitrary graphs. The main part of the algorithmconsists in the determination of upper bounds by graph colorings. Using a modification of a known graph coloring method called heuristic greedy we simultaneously derive lower and upper bounds for the clique number. [ABSTRACT FROM AUTHOR]

Approximation of multi-extremum functions problems arise in a wide range of fields including deterministic and stochastic technics, multi-parameter optimization. The main focus of this thesis is to consider the different principle of approximation problem. Experimental definition of external impacts on complex technogenic systems is related to technical difficulties. This area of research arises from the fact that many problems of multi-extremum optimization are known to be computationally difficult (NP-hard). We propose a new approach to approximation based on the special (in particular, spherically symmetric infinite differentiable) functions. We design new approximation algorithms on structured and unstructured meshes which can be used as a tool for multi-extremum optimization. These algorithms are simple to implement and they are stable. The freedom choice of a basic function provides an opportunity to create the analytical expression for several practical models. The theoretical formulas of the proposed algorithm are derived, and the simulations results are presented. A problem of the environmental factors impacts on complex technogenic systems is used to demonstrate the possibilities of computational tools developed. The results computed for pilot studies are presented with the usage of various components of multiextremum approximation. Furthermore, these algorithms are suitable for applying approximation techniques for implementation on parallel computers. [ABSTRACT FROM AUTHOR]

The method of functional-voxel modeling (FVM) to build a voxel-geometric model is considered. The FVM method is based on the linear approximation of the area describing the geometric object by an algebraic function. In the process of approximation, the normalized coefficients are determined with the arguments of the obtained linear function at each point of the area. These coefficients are local geometric characteristics and are displayed using the corresponding color in the voxel space. [ABSTRACT FROM AUTHOR]

The traditional Canny algorithm has poor self-adaptability threshold, and it is more sensitive to noise. In order to overcome these drawbacks, this paper proposed a new edge detection method based on Canny algorithm. Firstly, the media filtering and filtering based on the method of Euclidean distance are adopted to process it; secondly using the Freichen algorithm to calculate gradient amplitude; finally, using the Otsu algorithm to calculate partial gradient amplitude operation to get images of thresholds value, then find the average of all thresholds that had been calculated, half of the average is high threshold value, and the half of the high threshold value is low threshold value. Experiment results show that this new method can effectively suppress noise disturbance, keep the edge information, and also improve the edge detection accuracy. [ABSTRACT FROM AUTHOR]

Temperature measurement by acoustic is a new kind of temperature visualization measurement technology. It is very important to develop three-dimensional temperature field reconstruction algorithm in acoustic application. In order to get the three-dimensional temperature field distribution, a three-dimensional temperature field reconstruction algorithm based on multi-objective optimization is proposed in this paper. A multi-objective optimization model is set up considering least square criterion, smoothness criterion and maximum entropy criterion. Numerical experiments show that the algorithm of image quality is improved obviously compared with that of least squares algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new global optimization algorithm for non-smooth unconstrained optimization problems. We construct a new global optimization (GO) method based on the function new smoothing technique. We give some numerical examples in order to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

A boundary integral framework for stochastic ray tracing in billiards was recently proposed in [1]. In particular, a phase-space boundary integral model for propagating uncertain ray or particle flows was described and shown to interpolate between deterministic and random models of the flow propagation. In this work we describe a discretisation scheme for this class of boundary transfer operators using piecewise constant collocation in the spatial variable and the Nyström method in the momentum variable. The simplicity of the spatial basis functions means that the corresponding spatial integration can be performed analytically. A range of quadrature rules are compared for the Nyström method and numerical results with relevance to high-frequency vibroacoustic wave problems are presented. [ABSTRACT FROM AUTHOR]

The selection process of master's applicants has serious implications for the University. Students must contribute to science through scientific publications in congresses and journals, increasing the institution levels. However, predicting its performance demand advisor's experience and time. The advisors must select from a group of projects which one is the best for both sides. This procedure can lead to wrong choices. With the purpose of enhancing the selection process of masters' students, this paper presents an analysis of the academic characteristics of master's education applicants. It was used to identify set of features that might help to predict an applicant's scientific production. Through the process of Knowledge Discovery in Database, was obtained a set of correlation rules between features and the expected applicant's performance. Data collection was made on the Lattes Platform (A Brazilian platform to Scientific Curricula). Then, after pre-processed, it was optimized using the Partition Around Medoids with Gower distance. Experiments were conducted by training seven classification algorithms in two approaches. The first was a naive approach, making the analysis as simple as possible and the second one, develop an optimized process of DM. The optimization used the clustering process to create a new main class for the classifiers. Results demonstrate that the optimized approach was able to achieve an accuracy around 85% using the Random Forest, while the naive has approximately 44%. Through the PAM algorithm, it was possible to determine the best attributes and highlight the main rules. The conclusion is that the category of University and their previous production was the most relevant attributes. It shows that is possible to predict the productivity of master's degree students. [ABSTRACT FROM AUTHOR]

In this work, we present a control-theoretic algorithm to improve the energy efficiency of the GPU targeting deadline-driven graphics applications. Our algorithm dynamically controls multiple power knobs within the GPU (DVFS and number of active slices) that have different control time granularities. We developed a multi-rate predictive control to overcome the time granularity constraints in the control variables and reduce runtime overhead. To enable predictive control, we developed runtime analytical predictive models for performance and power of the GPU, that take input from hardware counters and temperature sensor readings. We evaluated our approach on the latest generation of Intel Core i5 platform. Our experimental results demonstrate significant average GPU energy savings of 25% compared to the state-of-the-art algorithm at negligible performance overhead. [ABSTRACT FROM AUTHOR]

Timing resilient designs can remove variation margins by adding error detecting logic (EDL) that detects timing errors when execution completes within a resiliency window. Speeding up near-critical-paths during logic synthesis can reduce the amount of EDL needed but at the cost of increasing logic area. This creates a logic optimization strategy called resynthesis. This paper proposes a gate-sizing based mixed integer geometric programming framework to analytically model and optimize paths during resynthesis. We evaluate our approach on a set of ISCAS89 benchmarks and compare the overall area improvement after resynthesis guided by our mathematical model versus a previously published naive brute-force approach. Our experimental results demonstrate that our approach achieves up to 11% larger average area improvement. [ABSTRACT FROM AUTHOR]

Error metrics are used to evaluate the quality of an approximated circuit or to trade-o several approximated candidates in design exploration. Precisely determining the error of an approximated circuit is a hard problem since the errors accumulate over time depending on the composition and nature of individual components. In this paper, we present methods based on model checking to precisely determine error behavior in sequential circuits that contain approximated combinational components. Our experiments show that such an analysis is very significant and crucial to properly deduce the effects of approximations. [ABSTRACT FROM AUTHOR]

Homotopy Analysis Method(HAM) provides a great freedom in choosing the initial approximation when obtaining the homotopy-series. Obviously, the better the initial approximation, the faster the convergence rate of the corresponding homotopy-series. In this research, we use the truncated Mth-order homotopy-series approximation which obtained from Laplace HAM as a new initial approximation to gain a better homotopy-series solution. This iteration approach is implemented in the frame of Laplace HAM is called iterative Laplace Homotopy Analysis Method(i-LHAM) which can greatly accelerate the convergence rate of homotopy-series. Some examples are examined in this research to highlight the convenience and effectiveness of i-LHAM. [ABSTRACT FROM AUTHOR]

The paper presents the application of the Galerkin method to solving the parametric integral equation system (PIES) on the example of Laplace's equation. The main aim of the paper is the analysis of the effectiveness of two methods for PIES solving: the collocation method and the Galerkin method. Researches were performed on two examples with analytical solutions. Tests concern mainly the accuracy of obtained numerical solutions and their stability. For both analyzed methods calculations were made with the various number of expressions in the approximation series, whilst in the collocation method two variants of the arrangement of collocation points were considered. We also compared the complexity of both methods using the execution time. [ABSTRACT FROM AUTHOR]

The paper is focused on analyzing the performance of the first grid connected PV system recently installed in Albania. The system is composed by two sets of 12 panels of poly crystalline silicon modules connected in parallel, each with open circuit voltage 44.8V DC and nominal power is 195Wp. Meteorological data are measured using a meteorological station DAVIS. Aim of the study is to define an empirical function best fitting the experimental relation between meteorological data and power output of the system. Two empirical energetic approximation functions, Durisch and Evans models, were tested and needed parameters calculated. Both approximation models predict higher values of output electrical energy compared with experimental ones. Good linear correlation between three sets of data shows that duly correction of Durisch and Evans model with an experimentally defined factor makes them suitable for describing the PV system. [ABSTRACT FROM AUTHOR]

We study the optimization problem faced by a perfectly informed principal in a Bayesian game, who reveals information to the players about the state of nature to obtain a desirable equilibrium. This signaling problem is the natural design question motivated by uncertainty in games and has attracted much recent attention. We present new hardness results for signaling problems in (a) Bayesian two-player zero-sum games, and (b) Bayesian network routing games. For Bayesian zero-sum games, when the principal seeks to maximize the equilibrium utility of a player, we show that it is NP-hard to obtain an additive FPTAS. Our hardness proof exploits duality and the equivalence of separation and optimization in a novel way. Further, we rule out an additive PTAS assuming planted clique hardness, which states that no polynomial time algorithm can recover a planted clique from an Erdős-Rényi random graph. Complementing these, we obtain a PTAS for a structured class of zero-sum games (where obtaining an FPTAS is still NP-hard) when the payoff matrices obey a Lipschitz condition. Previous results ruled out an FPTAS assuming planted-clique hardness, and a PTAS only for implicit games with quasi-polynomial-size strategy sets. For Bayesian network routing games, wherein the principal seeks to minimize the average latency of the Nash flow, we show that it is NP-hard to obtain a (multiplicative) (4/3 - ϵ)-approximation, even for linear latency functions. This is the optimal inapproximability result for linear latencies, since we show that full revelation achieves a 4/3-approximation for linear latencies. [ABSTRACT FROM AUTHOR]

Many algorithms, that are originally designed without explicitly considering incentive properties, are later combined with simple pricing rules and used as mechanisms. The resulting mechanisms are often natural and simple to understand. But how good are these algorithms as mechanisms? Truthful reporting of valuations is typically not a dominant strategy (certainly not with a pay-your-bid, first-price rule, but it is likely not a good strategy even with a critical value, or second-price style rule either). Our goal is to show that a wide class of approximation algorithms yields this way mechanisms with low Price of Anarchy. The seminal result of Lucier and Borodin [2010] shows that combining a greedy algorithm that is an-approximation algorithm with a pay-your-bid payment rule yields a mechanism whose Price of Anarchy is O(α). In this paper we significantly extend the class of algorithms for which such a result is available by showing that this close connection between approximation ratio on the one hand and Price of Anarchy on the other also holds for the design principle of relaxation and rounding provided that the relaxation is smooth and the rounding is oblivious. We demonstrate the far-reaching consequences of our result by showing its implications for sparse packing integer programs, such as multi-unit auctions and generalized matching, for the maximum traveling salesman problem, for combinatorial auctions, and for single source unsplittable flow problems. In all these problems our approach leads to novel simple, near-optimal mechanisms whose Price of Anarchy either matches or beats the performance guarantees of known mechanisms. [ABSTRACT FROM AUTHOR]

We study the problem of setting a price for a potential buyer with a valuation drawn from an unknown distribution D. The seller has "data" about D in the form of m ≥ 1 i.i.d. samples, and the algorithmic challenge is to use these samples to obtain expected revenue as close as possible to what could be achieved with advance knowledge of D. Our first set of results quantifies the number of samples m that are necessary and sufficient to obtain a (1-ε)-approximation. For example, for an unknown distribution that satisfies the monotone hazard rate (MHR) condition, we prove that Θ(ε-3/2) samples are necessary and sufficient. Remarkably, this is fewer samples than is necessary to accurately estimate the expected revenue obtained for such a distribution by even a single reserve price. We also prove essentially tight sample complexity bounds for regular distributions, bounded-support distributions, and a wide class of irregular distributions. Our lower bound approach, which applies to all randomized pricing strategies, borrows tools from differential privacy and information theory, and we believe it could find further applications in auction theory. Our second set of results considers the single-sample case. While no deterministic pricing strategy is better than 1/2-approximate for regular distributions, for MHR distributions we show how to do better: there is a simple deterministic pricing strategy that guarantees expected revenue at least 0.589 times the maximum possible. We also prove that no deterministic pricing strategy achieves an approximation guarantee better than e/4 ≈.68. [ABSTRACT FROM AUTHOR]

More and more smartphone applications need microdata, but publishing a microdata table may leak respondents' privacy. Conventional research on Privacy Preserving Data Publishing (PPDP) focuses on providing an identical privacy protection. Consider that, instead of being trapped in a small coterie, information usually propagates from friend to friend. In this paper, we study the PPDP problem on a mobile social network: along a table's propagation path, how can we create a series of tables with increasing privacy protection levels? The tradeoff between the created tables' overall utility and their individual privacy requirements are not trivial, especially for distributed systems: any inappropriate sanitization operation under a lower privacy-level may cause dramatic utility loss on subsequent tables. In order to solve the problem, we propose an approximation algorithm by previewing the future privacy requirements. Simulation results show that our scheme can successfully increase the overall utility, and meet the strengthening privacy protection requirements. [ABSTRACT FROM AUTHOR]

Many video processing algorithms are formulated as least-squares problems that result in large, sparse linear systems. Solving such systems in real time is very demanding. This paper focuses on reducing the computational complexity of a direct Cholesky-decomposition-based solver. Our approximation scheme builds on the observation that, in well-conditioned problems, many elements in the decomposition nearly vanish. Such elements may be pruned from the dependency graph with mild accuracy degradation. Using an example from image-domain warping, we show that pruning reduces the amount of operations per solve by over 75 %, resulting in significant savings in computing time, area or energy. [ABSTRACT FROM AUTHOR]

In this paper, we initiate the study of the multiplicative bidding language adopted by major Internet search companies. In multiplicative bidding, the effective bid on a particular search auction is the product of a base bid and bid adjustments that are dependent on features of the search (for example, the geographic location of the user, or the platform on which the search is conducted). We consider the task faced by the advertiser when setting these bid adjustments, and establish a foundational optimization problem that captures the core difficulty of bidding under this language. We give matching algorithmic and approximation hardness results for this problem; these results are against an information-theoretic bound, and thus have implications on the power of the multiplicative bidding language itself. Inspired by empirical studies of search engine price data, we then codify the relevant restrictions of the problem, and give further algorithmic and hardness results. Our main technical contribution is an O(log n)-approximation for the case of multiplicative prices and monotone values. We also provide empirical validations of our problem restrictions, and test our algorithms on real data against natural benchmarks. Our experiments show that they perform favorably compared with the baseline. [ABSTRACT FROM AUTHOR]

Expo-rational B-splines (ERBS) provide a blending type construction where local functions at each knot are blended together by infinitely smooth basis functions. In this work we consider some specific ERBS curves that are approximations of parametric curves. We study local refinement to increase flexibility by inserting local control curves at points of interest on the ERBS curve. Inserting knots into an existing B-spline knot vector results in a new spline space which contains the original spline space as a sub-space. In contrast to B-splines, knot insertion with ERBS results in a rational local function. We investigate methods to generate local curves of different brands. Using this, we blend extra local curves with the original ERBS curve, by knot insertion, and compare the differences with respect to geometric shape and approximation errors. [ABSTRACT FROM AUTHOR]