Relevant bibliographies by topics / Implementation and Optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Implementation and Optimization'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Implementation and Optimization"

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Bassil, Youssef. "Implementation of Combinatorial Algorithms using Optimization Techniques." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 660–66. http://dx.doi.org/10.31142/ijtsrd22925.

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Gianamore, David. "Advanced control implementation optimization." ISA Transactions 30, no. 2 (January 1991): 69–74. http://dx.doi.org/10.1016/0019-0578(91)90042-4.

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Vaughn, Susan. "Optimization Education After Project Implementation." Journal for Nurses in Staff Development (JNSD) 27, no. 2 (March 2011): E1—E4. http://dx.doi.org/10.1097/nnd.0b013e31820eefe4.

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Lindquist, Will, David Darwin, JoAnn Browning, Heather A. K. McLeod, Jiqiu Yuan, and Diane Reynolds. "Implementation of concrete aggregate optimization." Construction and Building Materials 74 (January 2015): 49–56. http://dx.doi.org/10.1016/j.conbuildmat.2014.10.027.

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Abed, Sa’ed, Reem Jaffal, Bassam Mohd, and Mohammad Alshayeji. "FPGA Modeling and Optimization of a SIMON Lightweight Block Cipher." Sensors 19, no. 4 (February 21, 2019): 913. http://dx.doi.org/10.3390/s19040913.

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Security of sensitive data exchanged between devices is essential. Low-resource devices (LRDs), designed for constrained environments, are increasingly becoming ubiquitous. Lightweight block ciphers provide confidentiality for LRDs by balancing the required security with minimal resource overhead. SIMON is a lightweight block cipher targeted for hardware implementations. The objective of this research is to implement, optimize, and model SIMON cipher design for LRDs, with an emphasis on energy and power, which are critical metrics for LRDs. Various implementations use field-programmable gate array (FPGA) technology. Two types of design implementations are examined: scalar and pipelined. Results show that scalar implementations require 39% less resources and 45% less power consumption. The pipelined implementations demonstrate 12 times the throughput and consume 31% less energy. Moreover, the most energy-efficient and optimum design is a two-round pipelined implementation, which consumes 31% of the best scalar’s implementation energy. The scalar design that consumes the least energy is a four-round implementation. The scalar design that uses the least area and power is the one-round implementation. Balancing energy and area, the two-round pipelined implementation is optimal for a continuous stream of data. One-round and two-round scalar implementations are recommended for intermittent data applications.
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Liu, Guoxiao, Keting Jia, Puwen Wei, and Lei Ju. "High-Performance Hardware Implementation of MPCitH and Picnic3." IACR Transactions on Cryptographic Hardware and Embedded Systems 2024, no. 2 (March 12, 2024): 190–214. http://dx.doi.org/10.46586/tches.v2024.i2.190-214.

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Picnic is a post-quantum digital signature, the security of which relies solely on symmetric-key primitives such as block ciphers and hash functions instead of number theoretic assumptions. One of the main concerns of Picnic is the large signature size. Although Katz et al.’s protocol (MPCitH-PP) significantly reduces the size of Picnic, the involvement of more parties in MPCitH-PP leads to longer signing/verification times and more hardware resources. This poses new challenges for implementing high-performance Picnic on resource-constrained FPGAs. So far as we know, current works on the hardware implementation of MPCitH-based signatures are compatible with 3 parties only. In this work, we investigate the optimization of the implementation of MPCitH-PP and successfully deploying MPCitH-PP with more than three parties on resource-constrained FPGAs, e.g., Xilinx Artix-7 and Kintex-7, for the first time. In particular, we propose a series of optimizations, which include pipelining and parallel optimization for MPCitH-PP and the optimization of the underlying symmetric primitives. Besides, we make a slight modification to the computation of the offline commitment, which can further reduce the number of computations of Keccak. These optimizations significantly improve the hardware performance of Picnic3. Signing messages on our FPGA takes 0.047 ms for the L1 security level, outperforming Picnic1 with hardware by a factor of about 5.3, which is the fastest implementation of post-quantum signatures as far as we know. Our FPGA implementation for the L5 security level takes 0.146 ms beating Picnic1 by a factor of 8.5, and outperforming Sphincs by a factor of 17.3.
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., Sharath D. "OPTIMIZATION AND IMPLEMENTATION OF PARALLEL SQUARER." International Journal of Research in Engineering and Technology 03, no. 06 (June 25, 2014): 423–29. http://dx.doi.org/10.15623/ijret.2014.0306078.

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Dai, Guangming, Maocai Wang, Lei Peng, and Ruijie Qin. "Implementation And Optimization For Tate Pairing." Intelligent Automation & Soft Computing 17, no. 5 (January 2011): 607–17. http://dx.doi.org/10.1080/10798587.2011.10643174.

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Liu, Yufeng, Xiangyang Xu, and Hao Su. "AES Algorithm Optimization and FPGA Implementation." IOP Conference Series: Earth and Environmental Science 267 (June 8, 2019): 042070. http://dx.doi.org/10.1088/1755-1315/267/4/042070.

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Chen, Xueyu, Ralph W. Pike, Thomas A. Hertwig, and Jack R. Hopper. "Optimal implementation of on-line optimization." Computers & Chemical Engineering 22 (March 1998): S435—S442. http://dx.doi.org/10.1016/s0098-1354(98)00085-4.

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Dissertations / Theses on the topic "Implementation and Optimization"

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Bruccoleri, Christian. "Flower constellation optimization and implementation." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2404.

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Jerez, Juan Luis. "Custom optimization algorithms for efficient hardware implementation." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/12791.

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The focus is on real-time optimal decision making with application in advanced control systems. These computationally intensive schemes, which involve the repeated solution of (convex) optimization problems within a sampling interval, require more efficient computational methods than currently available for extending their application to highly dynamical systems and setups with resource-constrained embedded computing platforms. A range of techniques are proposed to exploit synergies between digital hardware, numerical analysis and algorithm design. These techniques build on top of parameterisable hardware code generation tools that generate VHDL code describing custom computing architectures for interior-point methods and a range of first-order constrained optimization methods. Since memory limitations are often important in embedded implementations we develop a custom storage scheme for KKT matrices arising in interior-point methods for control, which reduces memory requirements significantly and prevents I/O bandwidth limitations from affecting the performance in our implementations. To take advantage of the trend towards parallel computing architectures and to exploit the special characteristics of our custom architectures we propose several high-level parallel optimal control schemes that can reduce computation time. A novel optimization formulation was devised for reducing the computational effort in solving certain problems independent of the computing platform used. In order to be able to solve optimization problems in fixed-point arithmetic, which is significantly more resource-efficient than floating-point, tailored linear algebra algorithms were developed for solving the linear systems that form the computational bottleneck in many optimization methods. These methods come with guarantees for reliable operation. We also provide finite-precision error analysis for fixed-point implementations of first-order methods that can be used to minimize the use of resources while meeting accuracy specifications. The suggested techniques are demonstrated on several practical examples, including a hardware-in-the-loop setup for optimization-based control of a large airliner.
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Irshad, Saba, and Purna Chandra Nepal. "Rise Over Thermal Estimation Algorithm Optimization and Implementation." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4023.

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The uplink load for the scheduling of Enhanced-Uplink (E-UL) channels determine the achievable data rate for Wideband Code Division Multiple Access (WCDMA) systems, therefore its accurate measurement carries a prime significance. The uplink load also known as Rise-over-Thermal (RoT), which is the quotient of the Received Total Wideband Power (RTWP) and the Thermal Noise Power floor. It is a major parameter which is calculated at each Transmission Time Interval (TTI) for maintaining cell coverage and stability. The RoT algorithm for evaluation of uplink load is considered as a complex and resource demanding among several Radio Resource Management (RRM) algorithms running in a radio system. The main focus of this thesis is to study RoT algorithm presently deployed in radio units and its possible optimization by reducing complexity of the algorithm in terms of memory usage and processing power. The calculation of RoT comprises three main blocks a Kalman filter, a noise floor estimator and the RoT computation. After analyzing the complexity of each block it has been established that the noise floor estimator block is consuming most of the processing power producing peak processor load since it involves many complex floating point calculations. However, the other blocks do not affect the processing load significantly. It was also observed that some block updates can be reduced in order to decrease the average load on the processor. Three techniques are proposed for reducing the complexity of the RoT algorithm, two for the reduction of peak load and one for the reduction of average load. For reducing the peak load, an interpolation approach is used instead of performing transcendental mathematical calculations. Also, the calculations involving noise floor estimation are extended over several TTIs by keeping in view that the estimation is not time critical. For the reduction of average load, the update rate for the Kalman Filter block is reduced. Based on these optimization steps, a modified algorithm for RoT computation with reduced complexity is proposed. The proposed changes are tested by means of MATLAB simulations demonstrating the improved performance with consistency in the output results. Finally, an arithmetic operation count is done using the hardware manual of Power PC (PPC405) used in Platform 4, which gives a rough estimate of decrease in the percentage of calculations after optimization.
saabairshad@gmail.com
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Karlsson, Victor, and Susanna Olsson. "Implementation of dynamic route optimization - drivers and barriers." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150134.

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Svevia is a company working with installation, occupancy and operation of infrastructure. They are currently testing and developing a new system called dynamic route optimization (DynOpt) in cooperation with B and M Systemutveckling. This system is able to contribute to a series of improvements for the company, such as automatization of certain processes, conclude demand on a more local level and create dynamic optimized routes which in the best way handles the determined local demands. Svevia sees great potentials of profits with the system, thereby has an interest of a support for decision-making regarding what may or may not be problematic during a potential implementation of such a system. This report regards a case study of the affects that DynOpt can have on its future users and what impact such effects might have on Svevia, with the goal of determining the drivers and barriers of DynOpt related to soft parameters. The soft parameters in this case study relate to how the changes DynOpt entails may affect the users and why or why not they may desire or be willing to accept these changes. The method used to conclude these drivers and barriers is to firstly gather information from the users by interviews and surveys, which then is analyzed in order to determine what advantages and disadvantages the users see with the system. The second step of the data processing consists of a SWOT-analysis execution. The strategic effects are determined through consultation with insight personnel. Lastly the remaining significant results are converted into drivers and barriers by firstly eliminating the information that may not pan out in any driver or barrier and secondly lumping together result describing similar effects into drivers and barriers. Eleven drivers and six barriers are concluded. Such results are for example the driver that the potential future users’ interest in technology may ease the implementation since DynOpt is a technological implementation. One of the barriers on the other hand is that the total driving distance will be reduced through optimization, which results in less available work for the chauffeurs which worsens their working conditions and can result in resistance to the implementation. This driver and barrier as well as the others in conjunction with a discussion, constitute the final result of the report and describe aspects that may hinder or make it easier to successfully implement DynOpt.
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Banjac, Goran. "Operator splitting methods for convex optimization : analysis and implementation." Thesis, University of Oxford, 2018. https://ora.ox.ac.uk/objects/uuid:17ac73af-9fdf-4cf6-a946-3048da3fc9c2.

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Convex optimization problems are a class of mathematical problems which arise in numerous applications. Although interior-point methods can in principle solve these problems efficiently, they may become intractable for solving large-scale problems or be unsuitable for real-time embedded applications. Iterations of operator splitting methods are relatively simple and computationally inexpensive, which makes them suitable for these applications. However, some of their known limitations are slow asymptotic convergence, sensitivity to ill-conditioning, and inability to detect infeasible problems. The aim of this thesis is to better understand operator splitting methods and to develop reliable software tools for convex optimization. The main analytical tool in our investigation of these methods is their characterization as the fixed-point iteration of a nonexpansive operator. The fixed-point theory of nonexpansive operators has been studied for several decades. By exploiting the properties of such an operator, it is possible to show that the alternating direction method of multipliers (ADMM) can detect infeasible problems. Although ADMM iterates diverge when the problem at hand is unsolvable, the differences between subsequent iterates converge to a constant vector which is also a certificate of primal and/or dual infeasibility. Reliable termination criteria for detecting infeasibility are proposed based on this result. Similar ideas are used to derive necessary and sufficient conditions for linear (geometric) convergence of an operator splitting method and a bound on the achievable convergence rate. The new bound turns out to be tight for the class of averaged operators. Next, the OSQP solver is presented. OSQP is a novel general-purpose solver for quadratic programs (QPs) based on ADMM. The solver is very robust, is able to detect infeasible problems, and has been extensively tested on many problem instances from a wide variety of application areas. Finally, operator splitting methods can also be effective in nonconvex optimization. The developed algorithm significantly outperforms a common approach based on convex relaxation of the original nonconvex problem.
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Safi, Mohammed. "Bridge Life Cycle Cost Optimization : Analysis, Evaluation & Implementation." Thesis, KTH, Civil and Architectural Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11908.

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In infrastructure construction projects especially bridge investments, the most critical decisions that significantly affect the whole bridge LCC are the early stages decisions. Clearly, it's more beneficial to correctly choose the optimum bridge than to choose the optimum construction or repair method.

The ability of a bridge to provide service over time demands appropriate maintenance by the agency. Thus the investment decision should consider not only the initial activity that creates a public good, but also all future activities that will be required to keep that investment available to the public.

This research is aiming for bridge sustainability, enhance the bridge related decision making, and facilitate the usage of the bridge related feedbacks. The development of a reliable and usable computer tool for bridge LCC & LCA evaluation is the main target.

Toward the main goal, many steps were fulfilled. A unique integrated Bridge LCC evaluation methodology was developed. Two systematic evaluation ways were developed, one for bridge user cost and one for the bridge aesthetical and cultural value. To put these two systematic ways in practice, two preliminary computer programs were developed for this purpose. Today and future works are focusing on developing methodology and preliminary computer tool for bridge agency cost as well as the bridge LCA evaluation. KTH unique LCC evaluation system will enable the decision makers to correctly choose the optimum bridge in the early stages decision making phases as well as any later on reparation method.


ETSI
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Abed, El-Fattah Safi Mohammed. "Bridge Life Cycle Cost Optimization : Analysis, Evaluation, & Implementation." Thesis, KTH, Bro- och stålbyggnad, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-36944.

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Müllner, Marie. "Optimization and implementation of gold-nanoparticles for medical imaging." Diss., Ludwig-Maximilians-Universität München, 2015. http://nbn-resolving.de/urn:nbn:de:bvb:19-182484.

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Gina, Ervin. "Implementation and Optimization of an Inverse Photoemission Spectroscopy Setup." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4050.

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Inverse photoemission spectroscopy (IPES) is utilized for determining the unoccupied electron states of materials. It is a complementary technique to the widely used photoemission spectroscopy (PES) as it analyzes what PES cannot, the states above the Fermi energy. This method is essential to investigating the structure of a solid and its states. IPES has a broad range of uses and is only recently being utilized. This thesis describes the setup, calibration and operation of an IPES experiment. The IPES setup consists of an electron gun which emits electrons towards a sample, where photons are released, which are measured in isochromat mode via a photon detector of a set energy bandwidth. By varying the electron energy at the source, a spectrum of the unoccupied density of states can be obtained. Since IPES is not commonly commercially available the design consists of many custom made components. The photon detector operates as a bandpass filter with a mixture of acetone/argon and a CaF2 window setting the cutoff energies. The counter electronics consist of a pre-amplifier, amplifier and analyzer to detect the count rate at each energy level above the Fermi energy. Along with designing the hardware components, a Labview program was written to capture and log the data for further analysis. The software features several operating modes including automated scanning which allows the user to enter the desired scan parameters and the program will scan the sample accordingly. Also implemented in the program is the control of various external components such as the electron gun and high voltage power supply. The new setup was tested for different gas mixtures and an optimum ratio was determined. Subsequently, IPES scans of several sample materials were performed for testing and optimization. A scan of Au was utilized for the determination of the Fermi edge energy and for comparison to literature spectra. The Fermi edge energy was then used in a measurement of indium tin oxide (ITO) determining the conduction band onset. This allowed the determination of the "transfer gap" of ITO. Future experiments will allow further application of IPES on materials and interfaces where characterization of their electronic structure is desired.
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Lee, Bin Hong Alex. "Empty container logistics optimization : an implementation framework and methods." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90715.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 68-70).
Empty container logistics is a huge cost component in an ocean carrier's operations. Managing this cost is important to ensure profitability of the business. This thesis proposes a 3-stage framework to handle empty container logistics with cost management as the objective. The first stage studies the forecasting of laden shipment demand, which provides the empty container supply requirement. Based on the supply needs, the problem of optimizing the fleet size was then addressed by using an inventory model to establish the optimal safety stock level. Simulations were used to understand the sensitivity of safety stock to desired service level. The final stage involves using mathematical programming to optimize repositioning costs incurred by carriers to ship empty containers to places which need them due to trade imbalance. At the same time, costs that are incurred due to leasing and storage are considered. A comparison between just-in-time and pre-emptive replenishment was performed and impact due to uncertainties is investigated. The framework is then implemented in a Decision Support System for an actual ocean carrier and is used to assist the empty container logistics team to take the best course of action in daily operations. The results from the optimizations show that there are opportunities for the carrier to reduce its fleet size and cut empty container logistics related costs.
by Bin Hong Alex Lee.
S.M. in Engineering and Management
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Books on the topic "Implementation and Optimization"

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1935-, Zheng Quan, ed. Integral global optimization: Theory, implementation, and applications. Berlin: Springer-Verlag, 1988.

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Chadderton, Ronald A. Implementation and optimization of distribution flushing programs. Denver, CO: AWWA Research Foundation and American Water Works Association, 1992.

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Web portal design, implementation, integration, and optimization. Hershey, PA: Information Science Reference, 2013.

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Advanced FPGA design: Architecture, implementation, and optimization. Hoboken, NJ: Wiley-Interscience, 2007.

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Davis, Justin, and Robert Reese. Finite State Machine Datapath Design, Optimization, and Implementation. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-79776-7.

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Camil, Demetrescu, Goldberg Andrew V, and Johnson David S. 1945-, eds. The shortest path problem: Ninth DIMACS implementation challenge. Providence, R.I: American Mathematical Society, 2009.

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Metaheuristics: From design to implementation. Hoboken, N.J: John Wiley & Sons, 2009.

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Vasfi, Gucer, and International Business Machines Corporation. International Technical Support Organization., eds. IBM Tivoli Monitoring: Implementation and performance optimization for large scale environments. [Poughkeepsie, NY]: International Technical Support Organizatioin, 2008.

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United States. National Aeronautics and Space Administration., ed. Implementation of a multiblock sensitivity analysis method in numerical aerodynamic shape optimization: Abstract. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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Jarke, Matthias. Query optimization in KBMS: Overview, research issues, and concepts for a Telos implementation. Toronto: University of Toronto, Dept. of Computer Science, 1989.

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Book chapters on the topic "Implementation and Optimization"

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Maimon, Oded, Eugene Khmelnitsky, and Konstantin Kogan. "Implementation Methodology." In Applied Optimization, 271–86. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2834-7_8.

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Hürlimann, Tony. "The Implementation." In Applied Optimization, 235–47. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-5793-4_9.

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Sciore, Edward. "Query Optimization." In Database Design and Implementation, 419–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33836-7_15.

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Sanchez, Ernesto, Massimiliano Schillaci, and Giovanni Squillero. "Implementation details." In Evolutionary Optimization: the µGP toolkit, 103–24. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09426-7_11.

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Tawarmalani, Mohit, and Nikolaos V. Sahinidis. "The Implementation." In Nonconvex Optimization and Its Applications, 213–28. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3532-1_7.

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Solnon, Christine, and Narendra Jussien. "Implementation Issues." In Ant Colony Optimization and Constraint Programming, 151–60. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557563.ch11.

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Ferris, Michael C., and Todd S. Munson. "Interfaces to PATH 3.0: Design, Implementation and Usage." In Computational Optimization, 207–27. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5197-3_11.

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Chapela, Victor, Regino Criado, Santiago Moral, and Miguel Romance. "Towards the Implementation of the Model." In SpringerBriefs in Optimization, 103–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26423-3_7.

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Higle, Julia L., and Suvrajeet Sen. "Guidelines for Computer Implementation." In Nonconvex Optimization and Its Applications, 165–83. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-4115-8_6.

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Chen, Ning, Zongming Yang, and Wenbin Luo. "Optimization of Key Components." In Development and Implementation of Ship BWMS, 111–67. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6865-1_6.

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Conference papers on the topic "Implementation and Optimization"

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Vitek, Jan. "Session details: Optimization." In PLDI '10: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/3254328.

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Hind, Michael. "Session details: Optimization." In PLDI05: ACM SIGPLAN Conference on Programming Language Design and Implementation 2005. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3249492.

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Hendren, Laurie. "Session details: Optimization." In PLDI '12: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2012. http://dx.doi.org/10.1145/3250588.

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Nabhan, M. Bassam, Nader Barsoum, G. W. Weber, and Pandian Vasant. "EFFECTIVE IMPLEMENTATION OF RELIABILITY CENTERED MAINTENANCE." In POWER CONTROL AND OPTIMIZATION: Proceedings of the 3rd Global Conference on Power Control and Optimization. AIP, 2010. http://dx.doi.org/10.1063/1.3459791.

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Tappeta, Ravindra, Gopivallabha Rao, and Peter Milanowski. "Practical Implementation of Robust Design Optimization." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-6980.

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Wu, Chenggang. "Session details: Loop optimization." In PLDI '12: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2012. http://dx.doi.org/10.1145/3250592.

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Pande, Santosh. "Session details: Code optimization II." In PLDI03: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244051.

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Ebcioglu, Ebcioglu. "Session details: Code optimization III." In PLDI03: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244052.

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Hall, Mary. "Session details: Code optimization I." In PLDI03: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244046.

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Braun, Robert, Peter Gage, Ilan Kroo, and Ian Sobieski. "Implementation and performance issues in collaborative optimization." In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4017.

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Reports on the topic "Implementation and Optimization"

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Muir, David F. Optimization and Implementation of Long Nerve Allografts. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada609747.

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Muir, David F. Optimization and Implementation of Long Nerve Allografts. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada589046.

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Cary D. Brock. Economic Implementation and Optimization of Secondary Oil Recovery. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/908227.

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Williams, Samuel, Dhiraj Kalamkar, Amik Singh, Anand Deshpande, Brian Straalen, Mikhail Smelyanskiy, Ann Almgren, Pradeep Dubey, John Shalf, and Leonid Oliker. Implementation and Optimization of miniGMG - a Compact Geometric Multigrid Benchmark. Office of Scientific and Technical Information (OSTI), December 2012. http://dx.doi.org/10.2172/1136783.

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Apostolatos, A., B. Keith, C. Soriano, and R. Rossi. D6.1 Deterministic optimization software. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.018.

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This deliverable focuses on the implementation of deterministic optimization algorithms and problem solvers within KRATOS open-source software. One of the main challenges of optimization algorithms in Finite-Element based optimization is how to get the gradient of response functions which are used as objective and constraints when this is not available in an explicit form. The idea is to use local sensitivity analysis to get the gradient of the response function(s)
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Durfee, Justin David, Christopher Rawls Frazier, Bryan Arguello, Alisa Bandlow, Jared Lee Gearhart, and Katherine A. Jones. Contingency Contractor Optimization Phase 3 Sustainment Cost by JCA Implementation Guide. Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1367436.

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Atz, Milos, and Florent Heidet. Implementation of an Orificing Optimization Algorithm in the DASSH Subchannel Analysis Code. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1896893.

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Sjostrom, Travis. Equation of State Optimization and Uncertainty Quantization: Implementation in the LANL EOS Production Code OpenSesame. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1893646.

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Howard, Heidi, Chad Helmle, Raina Dwivedi, and Daniel Gambill. Stormwater Management and Optimization Toolbox. Engineer Research and Development Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/39480.

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As stormwater regulations for hydrologic and water quality control become increasingly stringent, Department of Defense (DoD) facilities are faced with the daunting task of complying with multiple laws and regulations. This often requires facilities to plan, design, and implement structural best management practices (BMPs) to capture, filter, and/or infiltrate runoff—requirements that can be complicated, contradictory, and difficult to plan. This project demonstrated the Stormwater Management Optimization Toolbox (SMOT), a spreadsheet-based tool that effectively analyzes and plans for compliance to the Energy Independence and Security Act (EISA) of 2007 pre-hydrologic conditions through BMP implementation, resulting in potential cost savings by reducing BMP sizes while simultaneously achieving compliance with multiple objectives. SMOT identifies the most cost-effective modeling method based on an installation’s local conditions (soils, rainfall patterns, drainage network, and regulatory requirements). The work first demonstrated that the Model Selection Tool (MST) recommendation accurately results in the minimum BMP cost for 45 facilities of widely varying climatic and regional conditions, and then demonstrated SMOT at two facilities.
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Brock P.E., Cary D. Economic Implementation and Optimization of Secondary Oil Recovery Process: St. Mary West Field, Lafayette County, Arkansas. Office of Scientific and Technical Information (OSTI), March 2003. http://dx.doi.org/10.2172/808644.

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