Algorithm Design Strategies Research Articles

Analysis method and algorithm design of biological sequence problem based on generalized k-mer vector

K-mer can be used for the description of biological sequences and k-mer distribution is a tool for solving sequences analysis problems in bioinformatics. We can use k-mer vector as a representation method of the k-mer distribution of the biological sequence. Problems, such as similarity calculations or sequence assembly, can be described in the k-mer vector space. It helps us to identify new features of an old sequence-based problem in bioinformatics and develop new algorithms using the concepts and methods from linear space theory. In this study, we defined the k-mer vector space for the generalized biological sequences. The meaning of corresponding vector operations is explained in the biological context. We presented the vector/matrix form of several widely seen sequence-based problems, including read quantification, sequence assembly, and pattern detection problem. Its advantages and disadvantages are discussed. Also, we implement a tool for the sequence assembly problem based on the concepts of k-mer vector methods. It shows the practicability and convenience of this algorithm design strategy.

FedPD: A Federated Learning Framework With Adaptivity to Non-IID Data

Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model, in which multiple local updates are performed using local data, before sending the local models to the cloud for aggregation. However, these schemes typically require strong assumptions, such as the local data are identically independent distributed (i.i.d), or the size of the local gradients are bounded. In this paper, we first explicitly characterize the behavior of the FedAvg algorithm, and show that without strong and unrealistic assumptions on the problem structure, the algorithm can behave erratically for non-convex problems (e.g., diverge to infinity). Aiming at designing FL algorithms that are provably fast and require as few assumptions as possible, we propose a new algorithm design strategy from the primal-dual optimization perspective. Our strategy yields a family of algorithms that take the same CTA model as existing algorithms, but they can deal with the non-convex objective, achieve the best possible optimization and communication complexity while being able to deal with both the full batch and mini-batch local computation models. Most importantly, the proposed algorithms are {\it communication efficient}, in the sense that the communication pattern can be adaptive to the level of heterogeneity among the local data. To the best of our knowledge, this is the first algorithmic framework for FL that achieves all the above properties.

Механізми формування та впровадження операційних стратегій у діяльності аграрних підприємств

Introduction. The volatility of the market environment requires agrarian enterprises to change existing ap-proaches to management. Increasing the competitiveness of such enterprises is possible through the development and implementation of appropriate strategies. Particular attention should be paid to marketing activities. The enterprise's operational strategy must be based on an effective production plan. The purpose of the article is to create mechanisms for generating and implementing operational strategies of ag-ricultural enterprises. Results. The importance of a comprehensive approach to the production plan formation of agrarian enterprise is substantiated in unstable economic conditions. There is no correlation between the strategic approaches evaluation and mathematical methods. Approaches to production planning in the process of agrarian enterprise activity are generalized. The objective functions of linear and nonlinear mathematical programming for operational management evaluation are determined. The importance of competitive strategy of agrarian enterprise for maintaining its position in the market has been noted. The factors of a competitive strategy formation of agrarian enterprise are given. The algorithm of strategies modeling of agrarian organization operational activity is developed. The stages of the strategic management system of agrarian enterprise operational activity have been presented and characterized. Alternatives to the production program optimization are offered. The methods of production planning in different market structures are systematized. Conclusions. The proposed algorithm design strategies for agrarian enterprise operational activity facilitate reasonable choice of strategy considering market conditions. Taking into account the specifics of consumer demand for-mation and factors of competitive reaction in the market will allow to determine the optimal ratio of prices and volumes of products. This will help coordinate local plans and optimize revenue. Keywords: operating strategy, strategic management, agrarian enterprise, economic activity, results of activity, production plan, marketing activity.

Research and Practice on Algorithm Analysis and Design Course Teaching Based on Online Judge

This paper describes the algorithms analysis and design course that uses ACM-ICPC programming competition problems as the basis for the lectures, homework and exams. Programming contest has certain timeliness, openness and objectivity of evaluation. By taking part in programming competitions students can improve solving problems and programming skills. The course of algorithm analysis and design presents the solution strategies of a number of specific issues (such as the sorting problems, search problems, path problems, combinatorial problems, etc.) so that students can master the algorithm design strategies and analysis methods. We prepare many problems for each issue in the online judgment system (OJS). By using OJS, we carry out all experiments and examinations in the specified times. After solving some problem, students submit the code to OJS through browser. We can output the student codes to further analysis in the background management system. Online judge system provides an open, independent teaching platform for students to learn the course knowledge. With OJS students can submit their code at any time and any where.

Using Planar Embedded DRAM in Memory Intensive Signal Processing Circuits: Case Studies on LDPC Decoding and Motion Estimation

This paper studies the feasibility and potential of using planar embedded DRAM (eDRAM), which is completely compatible with CMOS logic process, to improve circuit implementation efficiency of memory-hungry signal processing algorithms. In spite of its apparent cell area efficiency advantage over SRAM, planar eDRAM is not being widely used in practice, mainly due to its very short retention time (e.g., few $\upmu $ s and even a few hundreds ns). In this work, we contend that short retention time may not necessarily be a fundamental issue for implementing signal processing algorithms because they typically handle streaming data, which exhibits regular and predictable data access patterns, and has a large algorithm/architecture design space. This study elaborates on the rationale and application of using a planar eDRAM in memory-hungry signal processing circuit implementations, and discusses the possible algorithm and architecture design strategies to better embrace the use of planar eDRAM. For the purpose of demonstration, we use low-density parity-check (LDPC) code decoding and motion estimation in video encoding as test vehicles. Beyond a straightforward SRAM replacement, we propose an interleaved read/write page-mode DRAM operation to reduce planar eDRAM energy consumption by leveraging LDPC code decoding data access pattern, and we investigate the potential of using planar eDRAM to enable a higher degree of image data reuse in motion estimation by proposing a folded scan structure to further improve its effectiveness. We carried out detailed planar eDRAM SPICE simulations at 45 nm node to obtain its characteristics, based on which we quantitatively evaluate the effectiveness of using planar eDRAM in these two case studies.

Demystifying MapReduce

Recent innovations in Big Data have enabled major strides forward in our ability to glean important insights from massive amounts of data, and to use these insights to make better decisions. Underlying many of these innovations is a computational paradigm known as MapReduce, which enables computational processes to be scaled up to very large sizes and to take advantage of cloud computing. While very powerful, MapReduce also requires a nontrivial shift in algorithm design strategies. In this paper we provide an overview of MapReduce and types of problems it is suited for. We discuss general strategies for designing MapReduce-based algorithms and provide an illustration using social media analytics.

Designing the master schedule for demand-adaptive transit systems

Demand-Adaptive Systems (DAS) display features of both traditional fixed-line bus services and purely on-demand systems such as dial-a-ride, that is they offer demand-responsive services within the framework of traditional scheduled bus transportation. A DAS bus line serves, on one hand, a given a set of compulsory stops according to a predefined schedule specifying the time windows associated to each, providing the traditional use of the transit line, without requiring any reservation. On the other hand, passengers may also issue requests for transportation between two desired, optional, stops, which induces detours in the vehicle routes. The design of a DAS line is a complex planning operation that requires to determine not only its design in terms of selecting the compulsory stops, but also its master schedule in terms of the time windows associated to the compulsory stops. Designing a DAS thus combines elements of strategic and tactical planning. In this paper we focus on determining a master-schedule for a single DAS line. We propose a mathematical description and a solution framework based on the estimation of a number of statistical parameters of the demand and the DAS line service. Results of numerical experiments are also given and analyzed. Cooperation as problem-solving and algorithm-design strategy is widely used to build methods addressing complex discrete optimization problems. In most cooperative-search algorithms, the explicit cooperation scheme yields a dynamic process not deliberately controlled by the algorithm design but inflecting the global behaviour of the cooperative solution strategy. The paper presents an overview of explicit cooperation mechanisms and describes issues related to the associated dynamic processes and the emergent computation they often generate. It also identifies a number of research directions into cooperation mechanisms, strategies for dynamic learning, automatic guidance, and self- adjustment, and the associated emergent computation processes.

Approach of generating parallel programs from parallelized algorithm design strategies

Today, parallel programming is dominated by message passing libraries, such as message passing interface (MPI). This article intends to simplify parallel programming by generating parallel programs from parallelized algorithm design strategies. It uses skeletons to abstract parallelized algorithm design strategies, as well as parallel architectures. Starting from problem specification, an abstract parallel abstract programming language+ (Apla+) program is generated from parallelized algorithm design strategies and problem-specific function definitions. By combining with parallel architectures, implicity of parallelism inside the parallelized algorithm design strategies is exploited. With implementation and transformation, C++ and parallel virtual machine (CPPVM) parallel program is finally generated. Parallelized branch and bound (B&B) algorithm design strategy and parallelized divide and conquer (D & C) algorithm design strategy are studied in this article as examples. And it also illustrates the approach with a case study.

"Upperview" algorithm design in teaching computer science in high schools

In this paper we are going to present a teaching/learning method and suggest a syllabus that help the high school students look at the algorithm design strategies from a so called "upperview": greedy, backtracking, divide and conquer, dynamic programming. The goal of the suggested syllabus is, beyond the presentation of the techniques, to offer the students a view that reveals them the basic and even the slight principal differences and similarities between the strategies. In consensus with the Comenius principle this is essential, if we want to master this field of programming ("To teach means scarcely anything more than to show how things differ from one another in their different purposes, forms, and origins. ... Therefore, he who differentiates well teaches well.").

On importance sampling in digital communications. II. Trellis-coded modulation

For Pt.I see ibid., vol.11, no.3, p.289-99 (1993). A simulation algorithm design strategy based on event simulation, conditional importance sampling, and mean translation biasing of Gaussian noise distribution is applied to systems with trellis-coded modulation using the error event simulation. Suboptimal Viterbi decoding in the presence of weak intersymbol interference (ISI) is discussed. To illustrate the utility of the technique, some numerical results for a nonlinear channel model with intersymbol interference (ISI) are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

On importance sampling in digital communications. I. Fundamentals

A simulation algorithm design strategy based on the combination of event simulation, conditional importance sampling, and asymptotically optimal biasing of Gaussian noise inputs is discussed. The utility of this approach is illustrated by presenting numerical results for a satellite channel model that includes uplink and downlink noise sources, a travelling-wave tube amplifier (TWTA) nonlinearity, and intersymbol interference (ISI) from both uplink and downlink filtering. An overview and comparison of various simulation design strategies and some results on the optimization of general mean translation and variance scaling biasing schemes for nonlinear systems are presented. >