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Java: An Introduction to Problem Solving &programming 6

2011年

作者： Walter Savitch Kenrick Mock

ISBN: (纸本)0132162709;9780132162708

For introductory Computer Science courses using Java, and other introductory programming courses in departments of Computer Science, Computer Engineering, CIS, MIS, IT, and Business. Students are introduced to object-oriented programming and important concepts such as design, testing and debugging, programming style, interfaces inheritance, and exception handling. The Java coverage is a concise, accessible introduction that covers key language features. Objects are covered thoroughly and early in the text, with an emphasis on application programs over applets. Updated for Java 7, the Sixth Edition also contains additional programming projects, case studies, and ***, Pearson's new online homework and assessment tool, is available with this edition.

关键词： programming solving problem introduction java

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programming

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2016年

programming by published by

关键词： howto make programming

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A multi-objective mathematical programming model for a novel capability-based university course timetabling problem

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2025年第1期40卷 365-379页

作者： Subula, Kemal Dokuz Eylul Univ Fac Engn Dept Ind Engn TR-35397 Izmir Turkiye

he university course timetabling is a tactical level problem that almost all academic departments encounterbefore each semester. Nowadays, in order to meet some standards, set by educational accreditation agencieswithin the scope of higher education quality assurance, it is required to generate efficient course timetables,which ensure optimal distribution of basic capabilities. Actually, these capabilities should be first specifiedbased on the learning outcomes defined by lecturers for each course that are also associated with programoutcomes and are aimed to be acquired by all of the students. Based on this motivation, a multi-objective mixed-integer non-linear programming model is developed for a novel capability-based course timetabling problem. Its validity and practicality are tested on a real-life application in an Industrial EngineeringDepartment. When the balanced solutions provided by compromise and fuzzy goal programming techniqueswere compared with the existing schedules of the previous years, it was revealed that significantimprovements could be achieved in terms of several conflicting objectives (i.e., optimal capabilitydistribution over the timetable, acquisition of maximum number/variety of different capabilities by thestudents, meeting expectations of lecturers by minimizing total temporal difference between the periodshis/her courses are assigned, total penalty cost related to soft constraints).

关键词： University course timetabling problem mixed integer non-linear programming multi-objective optimization capability-based approach compromise programming

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Weighted Sum-Rate Maximization With Transceiver and Passive Beamforming Design for IRS-Aided MIMO-BC Communications via Matrix Fractional programming

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第2期73卷 1383-1398页

作者： Qiu, Jing Yu, Jiguo Dong, Anming Yu, Kan Chen, Honglong Qufu Normal Univ Sch Math Sci Qufu 273165 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Qilu Univ Technol Big Data Inst Jinan 250353 Peoples R China Qilu Univ Technol Key Lab Comp Power Network & Informat Secur Shandong Comp Sci Ctr Shandong Acad SciMinist Educ Jinan 250353 Shandong Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan 250353 Shandong Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China China Univ Petr Coll Control Sci & Engn Qingdao 266580 Peoples R China

This paper investigates the joint active transceiver and passive beamforming design to maximize the weighted sum-rate (WSR) of an IRS-aided multi-streams multiuser multiple-input multiple-output broadcast channel (MIMO-BC) downlink transmission system. Due to the coupling of the transceiver parameters, the considered WSR optimization problem is highly non-convex and thus challenging to solve. Different from the normally used methods, such as the weighted minimum mean-square error (WMMSE), we rely on the matrix fractional programming (MFP) theory to derive an effective algorithm to the WSR problem. Specifically, we reformulate the original problem into a tractable one by exploiting the special structure of the objective function, i.e., a MFP which involves a matrix ratio inside a logarithm in the objective function. An alternating optimization (AO) framework is then devised to decompose the reformulated problem into four subproblems, which optimize the introduced auxiliary variable, the transmit beamforming matrix, the receive matrix, and the reflecting beamforming matrix by fixing other variables respectively. Through the matrix quadratic transform, we reformulate the MFP problem as a convex one, and thus obtain the optimal transmit beamforming matrix. By leveraging the optimality conditions for unconstrained optimization problems, the optimal receive beamforming matrix and the introduced auxiliary variable are derived in closed form. For solving the passive beamforming subproblem, we propose an iterative algorithm based on successive convex approximation (SCA). Since the computational complexity of SCA is relatively high, we propose a computationally efficient method based on manifold optimization (MO) to optimize the passive beamforming matrix. Finally, we also consider the robust beamforming design when the system suffers from imperfect CSI. Simulation results demonstrate the effectiveness of the proposed methods.

关键词： Optimization Array signal processing Transceivers MIMO communication Transforms programming Matrix decomposition Matrix Fractional programming (MFP) alternating optimization (AO) intelligent reflecting surface (IRS) manifold optimization (MO) beamforming

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Multiform Genetic programming Framework for Symbolic Regression Problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第2期29卷 429-443页

作者： Zhong, Jinghui Dong, Junlan Liu, Wei-Li Feng, Liang Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510006 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300071 Peoples R China Hanyang Univ ERICA Seoul 15588 South Korea

Genetic programming (GP) is a widely recognized and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on a single form to solve the problem, which limits their search diversity and increases the likelihood of getting stuck in local optima, especially in complex scenarios. In this article, we propose a general multiform GP (MFGP) framework to improve the performance of GP on complicated SR problems. As far as we know, this articel is the first attempt to integrate the multiform optimization paradigm with GP to accelerate the search performance. The key idea of the proposed framework is to construct multiple forms to solve the same problem cooperatively at the same time. During the evolution process, knowledge gained from different forms is shared among the solvers to improve the search diversity and efficiency. A knowledge transfer mechanism is specifically designed to facilitate knowledge transfer among GP solvers with different modeling forms. In addition, an adaptive resource control mechanism is designed to reallocate computing resources according to the problem solving efficiency of different solvers to further improve search efficiency. To demonstrate the effectiveness of the proposed framework, a multiform gene expression programming algorithm is designed and tested on 20 problems, including physical datasets, synthetic datasets, and real-world datasets. The experimental results have demonstrated the effectiveness of the proposed framework.

关键词： Optimization Search problems Multitasking Adaptation models Semantics programming Mathematical models Transfer learning Resource management Knowledge transfer Genetic programming (GP) multiform optimization multitasking optimization transfer learning (TL) symbolic regression (SR)

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LiP-LLM: Integrating Linear programming and Dependency Graph With Large Language Models for Multi-Robot Task Planning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1122-1129页

作者： Obata, Kazuma Aoki, Tatsuya Horii, Takato Taniguchi, Tadahiro Nagai, Takayuki Osaka Univ Grad Sch Engn Sci Dept Syst Innovat Osaka 5650871 Japan Kyoto Univ Dept Informat Kyoto 6068501 Japan

This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. For multi-robots to efficiently perform tasks, it is necessary to manage the precedence dependencies between tasks. Although multi-robot decentralized and centralized task planners using LLMs have been proposed, none of these studies focus on precedence dependencies from the perspective of task efficiency or leverage traditional optimization methods. It addresses key challenges in managing dependencies between skills and optimizing task allocation. LiP-LLM consists of three steps: skill list generation and dependency graph generation by LLMs, as well as task allocation using linear programming. The LLMs are utilized to generate a comprehensive list of skills and to construct a dependency graph that maps the relationships and sequential constraints among these skills. To ensure the feasibility and efficiency of skill execution, the skill list is generated by calculated likelihood, and linear programming is used to optimally allocate tasks to each robot. Experimental evaluations in simulated environments demonstrate that this method outperforms existing task planners, achieving higher success rates and efficiency in executing complex, multi-robot tasks. The results indicate the potential of combining LLMs with optimization techniques to enhance the capabilities of multi-robot systems in executing coordinated tasks accurately and efficiently. In an environment with two robots, a maximum success rate difference of 0.82 is observed in the language instruction group with a change in the object name.

关键词： Robots Planning Resource management Linear programming Robot kinematics Natural languages Multi-robot systems Optimization programming Navigation task planning

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Operational Coordination Optimization of Electricity and Natural Gas Networks Based on Sequential Symmetrical Second-Order Cone programming

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2025年第2期13卷 488-499页

作者： Min, Liang Lou, Chengwei Yang, Jin Yu, James Yu, Zhibin Power Grid Dispatching & Control Branch Co NARI Te Nanjing Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China Univ Glasgow Sch Engn Glasgow G12 8QQ Scotland Scottish Power Energy Network Glasgow G2 5AD Scotland Univ Liverpool Dept Mech & Aerosp Engn Liverpool England

The variable and unpredictable nature of renewable energy generation (REG) presents challenges to its large-scale integration and the efficient and economic operation of the electricity network, particularly at the distribution level. In this paper, an operational coordination optimization method is proposed for the electricity and natural gas networks, aiming to overcome the identified negative impacts. The method involves the implementation of bi-directional energy flows through power-to-gas units and gas-fired power plants. A detailed model of the three-phase power distribution system up to each phase is employed to improve the representation of multi-energy systems to consider real-world end-user consumption. This method allows for the full consideration of unbalanced operational scenarios. Meanwhile, the natural gas network is modelled and analyzed with steady-state gas flows and the dynamics of the line pack in pipelines. The sequential symmetrical second-order cone programming (SS-SOCP) method is employed to facilitate the simultaneous analysis of three-phase imbalance and line pack while accelerating the solution process. The efficacy of the operational coordination optimization method is demonstrated in case studies comprising a modified IEEE 123-node power distribution system with a 20-node natural gas network. The studies show that the operational coordination optimization method can simultaneously minimize the total operational cost, the curtailment of installed REG, the voltage imbalance of three-phase power system, and the overall carbon emissions.

关键词： Natural gas Power systems Pipelines Electricity Optimization Mathematical models Load flow programming Numerical models Power system dynamics Operational coordination multi-energy system power-to-gas electricity network natural gas network second-order cone programming

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To Abstract or Not to Abstract? A Comparative Study Evaluating the User Experience of Spreadsheet programming with Sheet-Defined Functional Abstractions

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第9期41卷 5445-5461页

作者： Kudebayeva, Anel Harte, Christopher Bryan-Kinns, Nick Stockman, Tony Queen Mary Univ London Dept Comp Sci London England Queen Mary Univ London London England Univ Arts London Creat Comp London England

Spreadsheets are a widespread functional programming paradigm that offer liveness and directness of interaction. However, spreadsheets are notoriously error-prone and difficult to debug. To overcome this limitation and improve the expressive power of spreadsheets, we propose an extension to the spreadsheet paradigm in the form of sheet-defined lambdas - user-defined functions that abstract computations on the sheet. This concept was developed and deployed in our web-based spreadsheet application named Lattice. We evaluate this approach through a user study which compared the user experience of programming in a spreadsheet with and without lambdas, as well as the difference in performance between learner (N = 12) and experienced (N = 12) programmers. The study measured participant task performance (task time, success rate and number of errors) and the quality of their user experience of using Lattice (video recordings of use, interviews and questionnaire responses). Our findings indicate that programming with lambdas is not only more efficient than writing formulas in a conventional way, but also provides a rewarding hedonic experience. However, we found that learners perceived the concept of functional abstractions with lambdas as difficult to comprehend;while experienced programmers noted potential utilitarian advantages that aid in managing the complexity of a spreadsheet program. The results obtained in this work contribute to a better understanding of human-spreadsheet interaction and can inform the future design of user-friendly computational systems.

关键词： User experience usability programming spreadsheet

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Competency-Based Hybrid Learning: A Modern Approach to Teaching programming and Digital Technologies Subjects

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IEEE ACCESS 2025年 13卷 54892-54919页

作者： Kucera, Erik Haffner, Oto Slovak Univ Technol Bratislava Fac Elect Engn & Informat Technol Bratislava 84104 Slovakia

The COVID-19 pandemic prompted the global education sector to experiment with various forms of online learning as institutions rapidly transitioned to remote formats. This article presents a comprehensive overview of a competency-based hybrid learning methodology, developed and implemented during the pandemic. In addition to detailing the methodology itself, the article also shares the experiences of educators at the authors' institution, who observed significant improvements in educational outcomes, surpassing even pre-pandemic standards. The methodology highlights the limitations of directly replicating traditional in-person instruction in an online format using existing materials and approaches. Instead, it advocates for carefully designed adaptations tailored to the digital environment, leveraging asynchronous components, interactive tools, and newly created e-learning resources to optimize effectiveness. This approach also requires increased interaction between educators and students beyond scheduled classes, ensuring timely support and guidance. Although this methodology may not suit all course types, it has proven particularly effective in advanced information and communication technologies (ICT) and digital technology subjects, such as programming, artificial intelligence, digital or electronic marketing, video editing, 3D engine work, etc. The positive student feedback further underscores the potential of this model to enhance educational quality and outcomes in these domains.

关键词： Hybrid learning Education programming profession programming Pandemics Engineering education Electronic learning Training Focusing COVID-19 online learning blended learning digital technologies ICT modern teaching training

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Analysis of ChatGPT-Generated Codes Across Multiple programming Languages

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IEEE ACCESS 2025年 13卷 23580-23596页

作者： Almanasra, Sally Suwais, Khaled Arab Open Univ Fac Comp Studies Riyadh 11681 Saudi Arabia

Our research focuses on the intersection of artificial intelligence (AI) and software development, particularly the role of AI models in automating code generation. With advancements in large language models like ChatGPT, developers can now generate code from natural language prompts, a task that traditionally required significant manual input and expertise. AI-generated code promises to boost productivity by enabling faster prototyping and automating repetitive coding tasks. However, as these models are increasingly adopted in real-world applications, questions surrounding their efficiency and code quality become critical. This research investigates ChatGPT-4o, a state-of-the-art language model, and its ability to generate functional, high-quality code in different programming languages. By comparing performance between Python and Java, the study seeks to shed light on AI's capabilities and limitations in code generation, addressing not only functional correctness but also broader software engineering concerns such as memory usage, runtime efficiency, and maintainability. The study addresses key questions related to the performance, code quality, and error management of AI-generated code by analyzing solutions for 300 data structure problems and 300 problems from the LeetCode platform. The findings reveal notable performance differences between the two languages: Java demonstrated superior runtime performance, particularly for medium and hard problems, while Python exhibited better memory efficiency across all complexity levels. The research also highlighted significant gaps in code quality, with both languages showing deficiencies in documentation and exception management. This study contributes to the literature by offering a comprehensive cross-language analysis of ChatGPT-4o's programming capabilities, addressing a gap in the evaluation of AI-generated code performance.

关键词： Codes programming Encoding Chatbots Runtime Python Software development management Java Complexity theory Standards Artificial intelligence software development ChatGPT large language model programming

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