Relevant bibliographies by topics / Optimization

Academic literature on the topic 'Optimization'

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Published: 4 June 2021
Last updated: 31 August 2024

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Journal articles on the topic "Optimization"

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Mukherjee, Manasij, and John Regehr. "Hydra: Generalizing Peephole Optimizations with Program Synthesis." Proceedings of the ACM on Programming Languages 8, OOPSLA1 (April 29, 2024): 725–53. http://dx.doi.org/10.1145/3649837.

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Optimizing compilers rely on peephole optimizations to simplify combinations of instructions and remove redundant instructions. Typically, a new peephole optimization is added when a compiler developer notices an optimization opportunity---a collection of dependent instructions that can be improved---and manually derives a more general rewrite rule that optimizes not only the original code, but also other, similar collections of instructions. In this paper, we present Hydra, a tool that automates the process of generalizing peephole optimizations using a collection of techniques centered on program synthesis. One of the most important problems we have solved is finding a version of each optimization that is independent of the bitwidths of the optimization's inputs (when this version exists). We show that Hydra can generalize 75% of the ungeneralized missed peephole optimizations that LLVM developers have posted to the LLVM project's issue tracker. All of Hydra's generalized peephole optimizations have been formally verified, and furthermore we can automatically turn them into C++ code that is suitable for inclusion in an LLVM pass.
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Sartoros, Christine, Douglas M. Goltz, and Eric D. Salin. "Program Considerations for Simplex Optimization of Ion Lenses in ICP-MS." Applied Spectroscopy 52, no. 5 (May 1998): 643–48. http://dx.doi.org/10.1366/0003702981944292.

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The performance of an inductively coupled plasma mass spectrometer (ICP-MS) is dependent on the ion optic bias potentials. A discussion of the multielement optimization of the ICP-MS ion optics bias potentials using a Simplex algorithm is presented. Three objective functions were tested: a function developed by Leary; the combined ratio method (CRM); and the Euclidean distance from multicriteria target vector optimization. Both the Leary and the target vector optimization's performances were comparable, whereas the CRM optimizations placed an emphasis on obtaining similar signal intensities. Experiments determined that an initial Simplex starting size of 20% of the parameter space was optimal. A method for the selection of an appropriate target vector by predicting analyte signal intensity was also investigated. Signal intensities for all elements could be predicted with an acceptable margin of error (10–30%), provided that the same conditions were used. Comparisons of optimizations using a single mid-mass element vs. multielement optimizations revealed that the multielement approach is only slightly better. If the analyst wished to optimize lens settings to favor heavy or light elements, then an average mass was better than a mid-mass optimization.
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Zhong, Mei Peng. "Parameter Optimization of Compressor Based on an Ant Colony Optimization." Applied Mechanics and Materials 201-202 (October 2012): 916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.916.

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A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pressure piston, the journey of high pressure piston and the rotate speed of BLDCM. Simulated result shows that the efficiency of BLDCM is more than that before optimizating. The test is done. The result shows that the specifc Power of air compressor is much less than before optimizating on 2.5Mpa. The result also shows that an Ant Colony Optimization which optimizating the air compressor is availability and practicality.
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Nan, Yang. "An Improved Ant Colony Optimization Algorithm Based on Immunization Strategy." Advanced Materials Research 490-495 (March 2012): 66–70. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.66.

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Ant colony optimization has been become a very useful method for combination optimization problems. Based on close connections between combination optimization and continuous optimization, nowadays some scholars have studied to apply ant colony optimization to continuous optimization problems, and proposed some continuous ant colony optimizations. To improve the performance of those continuous ant colony optimizations, here the principles of evolutionary algorithm and artificial immune algorithm have been combined with the typical continuous Ant Colony Optimization, and the adaptive Cauchi mutation and thickness selection are used to operate the ant individual, so a new Immunized Ant Colony Optimization is proposed.
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Rajeanderan Revichandran, Jaffar Syed Mohamed Ali, Moumen Idres, and A. K. M. Mohiuddin. "A Review of HVAC System Optimization and Its Effects on Saving Total Energy Utilization of a Building." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 93, no. 1 (March 25, 2022): 64–82. http://dx.doi.org/10.37934/arfmts.93.1.6482.

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The paper illustrates the review on the optimizations studies of HVAC systems based on three main methods – HVAC operational variables optimization, optimization of control parameters in HVAC system and parameter optimization in building models. For the HVAC system’s operational variables, the optimization process is based on the common and prescient energy utilization models. Thus, by comparing both, the non-common HVAC system models can get better output of energy reduction. Based on most of the studies, the occupancies thermal comfort requirements, are represented by the indoor air quality (IAQ) or the predicted mean vote (PMV) indexes. Comparing both requirements, the PMV index had a better overall energy reduction output of 47% and estimated annual energy reduction of 2,769 kg/year. Meanwhile, in optimization of HVAC’s control parameters, its overall aim is to achieve a better response output of the HVAC system in order to prevent energy wastage. Among this different optimization’s controller, the fuzzy logic tuning optimization has a better overall energy reduction. On the other hand, the parameter optimization in building model approach is performed before the construction of the structure itself, where multiple construction parameters are considerations in the design. In overall, when different tools for building parameter and model optimization are compared, the EXRETopt by using PMV comfort index approximately reduces 62% of the energy utilization.
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Seth, Ashish. "Regression Test Case Optimization Using Ant Colony Optimization." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (March 20, 2020): 111–18. http://dx.doi.org/10.5373/jardcs/v12i3/20201172.

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Batay, Sagidolla, Bagdaulet Kamalov, Dinmukhamed Zhangaskanov, Yong Zhao, Dongming Wei, Tongming Zhou, and Xiaohui Su. "Adjoint-Based High-Fidelity Concurrent Aerodynamic Design Optimization of Wind Turbine." Fluids 8, no. 3 (February 28, 2023): 85. http://dx.doi.org/10.3390/fluids8030085.

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To evaluate novel turbine designs, the wind energy sector extensively depends on computational fluid dynamics (CFD). To use CFD in the design optimization process, where lower-fidelity approaches such as blade element momentum (BEM) are more popular, new tools to increase the accuracy must be developed as the latest wind turbines are larger and the aerodynamics and structural dynamics become more complex. In the present study, a new concurrent aerodynamic shape optimization approach towards multidisciplinary design optimization (MDO) that uses a Reynolds-averaged Navier–Stokes solver in conjunction with a numerical optimization methodology is introduced. A multidisciplinary design optimization tool called DAFoam is used for the NREL phase VI turbine as a baseline geometry. Aerodynamic design optimizations in terms of five different schemes, namely, cross-sectional shape, pitch angle, twist, chord length, and dihedral optimization are conducted. Pointwise, a commercial mesh generator is used to create the numerical meshes. As the adjoint approach is strongly reliant on the mesh quality, up to 17.8 million mesh cells were employed during the mesh convergence and result validation processes, whereas 2.65 million mesh cells were used throughout the design optimization due to the computational cost. The Sparse Nonlinear OPTimizer (SNOPT) is used for the optimization process in the adjoint solver. The torque in the tangential direction is the optimization’s merit function and excellent results are achieved, which shows the promising prospect of applying this approach for transient MDO. This work represents the first attempt to implement DAFoam for wind turbine aerodynamic design optimization.
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Masarik, Jozef. "A thermodynamically motivated optimization algorithm: Circular wheel balance optimization." Applications of Mathematics 30, no. 6 (1985): 413–24. http://dx.doi.org/10.21136/am.1985.104171.

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Xu, Liansong, and Dazhi Pan. "Multi-objective Optimization Based on Chaotic Particle Swarm Optimization." International Journal of Machine Learning and Computing 8, no. 3 (June 2018): 229–35. http://dx.doi.org/10.18178/ijmlc.2018.8.3.692.

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Celik, Yuksel, and Erkan Ulker. "An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/370172.

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Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
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Dissertations / Theses on the topic "Optimization"

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Pieume, Calice Olivier. "Multiobjective optimization approaches in bilevel optimization." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00665605.

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This thesis addresses two important classes of optimization : multiobjective optimization and bilevel optimization. The investigation concerns their solution methods, applications, and possible links between them. First of all, we develop a procedure for solving Multiple Objective Linear Programming Problems (MOLPP). The method is based on a new characterization of efficient faces. It exploits the connectedness property of the set of ideal tableaux associated to degenerated points in the case of degeneracy. We also develop an approach for solving Bilevel Linear Programming Problems (BLPP). It is based on the result that an optimal solution of the BLPP is reachable at an extreme point of the underlying region. Consequently, we develop a pivoting technique to find the global optimal solution on an expanded tableau that represents the data of the BLPP. The solutions obtained by our algorithm on some problems available in the literature show that these problems were until now wrongly solved. Some applications of these two areas of optimization problems are explored. An application of multicriteria optimization techniques for finding an optimal planning for the distribution of electrical energy in Cameroon is provided. Similary, a bilevel optimization model that could permit to protect any economic sector where local initiatives are threatened is proposed. Finally, the relationship between the two classes of optimization is investigated. We first look at the conditions that guarantee that the optimal solution of a given BPP is Pareto optimal for both upper and lower level objective functions. We then introduce a new relation that establishes a link between MOLPP and BLPP. Moreover, we show that, to solve a BPP, it is possible to solve two artificial M0PPs. In addition, we explore Bilevel Multiobjective Programming Problem (BMPP), a case of BPP where each decision maker (DM) has more than one objective function. Given a MPP, we show how to construct two artificial M0PPs such that any point that is efficient for both problems is also efficient for the BMPP. For the linear case specially, we introduce an artificial MOLPP such that its resolution can permit to generate the whole feasible set of the leader DM. Based on this result and depending on whether the leader can evaluate or not his preferences for his different objective functions, two approaches for obtaining efficient solutions are presented
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Ruan, Ning. "Global optimization for nonconvex optimization problems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1936.

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Duality is one of the most successful ideas in modern science [46] [91]. It is essential in natural phenomena, particularly, in physics and mathematics [39] [94] [96]. In this thesis, we consider the canonical duality theory for several classes of optimization problems.The first problem that we consider is a general sum of fourth-order polynomial minimization problem. This problem arises extensively in engineering and science, including database analysis, computational biology, sensor network communications, nonconvex mechanics, and ecology. We first show that this global optimization problem is actually equivalent to a discretized minimal potential variational problem in large deformation mechanics. Therefore, a general analytical solution is proposed by using the canonical duality theory.The second problem that we consider is a nonconvex quadratic-exponential optimization problem. By using the canonical duality theory, the nonconvex primal problem in n-dimensional space can be converted into a one-dimensional canonical dual problem, which is either a concave maximization or a convex minimization problem with zero duality gap. Several examples are solved so as to illustrate the applicability of the theory developed.The third problem that we consider is quadratic minimization problems subjected to either box or integer constraints. Results show that these nonconvex problems can be converted into concave maximization dual problems over convex feasible spaces without duality gap and the Boolean integer programming problem is actually equivalent to a critical point problem in continuous space. These dual problems can be solved under certain conditions. Both existence and uniqueness of the canonical dual solutions are presented. A canonical duality algorithm is presented and applications are illustrated.The fourth problem that we consider is a quadratic discrete value selection problem subjected to inequality constraints. The problem is first transformed into a quadratic 0-1 integer programming problem. The dual problem is thus constructed by using the canonical duality theory. Under appropriate conditions, this dual problem is a maximization problem of a concave function over a convex continuous space. Theoretical results show that the canonical duality theory can either provide a global optimization solution, or an optimal lower bound approximation to this NP-hard problem. Numerical simulation studies, including some relatively large scale problems, are carried out so as to demonstrate the effectiveness and efficiency of the canonical duality method. An open problem for understanding NP-hard problems is proposed.The fifth problem that we consider is a mixed-integer quadratic minimization problem with fixed cost terms. We show that this well-known NP-hard problem in R2n can be transformed into a continuous concave maximization dual problem over a convex feasible subset of Rn with zero duality gap. We also discuss connections between the proposed canonical duality theory approach and the classical Lagrangian duality approach. The resulting canonical dual problem can be solved under certain conditions, by traditional convex programming methods. Conditions for the existence and uniqueness of global optimal solutions are presented. An application to a decoupled mixed-integer problem is used to illustrate the derivation of analytic solutions for globally minimizing the objective function. Numerical examples for both decoupled and general mixed-integral problems are presented, and an open problem is proposed for future study.The sixth problem that we consider is a general nonconvex quadratic minimization problem with nonconvex constraints. By using the canonical dual transformation, the nonconvex primal problem can be converted into a canonical dual problem (i.e., either a concave maximization problem with zero duality gap). Illustrative applications to quadratic minimization with multiple quadratic constraints, box/integer constraints, and general nonconvex polynomial constraints are discussed, along with insightful connections to classical Lagrangian duality. Conditions for ensuring the existence and uniqueness of global optimal solutions are presented. Several numerical examples are solved.The seventh problem that we consider is a general nonlinear algebraic system. By using the least square method, the nonlinear system of m quadratic equations in n-dimensional space is first formulated as a nonconvex optimization problem. We then prove that, by using the canonical duality theory, this nonconvex problem is equivalent to a concave maximization problem in Rm, which can be solved by well-developed convex optimization techniques. Both existence and uniqueness of global optimal solutions are discussed, and several illustrative examples are presented.The eighth problem that we consider is a general sensor network localization problem. It is shown that by the canonical duality theory, this nonconvex minimization problem is equivalent to a concave maximization problem over a convex set in a symmetrical matrix space, and hence can be solved by combining a perturbation technique with existing optimization techniques. Applications are illustrated and results show that the proposed method is potentially a powerful one for large-scale sensor network localization problems.
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Gedin, Sebastian. "Securities settlement optimization using an optimization software solution." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279295.

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Many people have engaged in the trading of stocks and other securities, but few are aware of how the transactions are executed. The process of transferring the ownership of securities, often in exchange for cash, is called securities settlement. If the parties involved in the transaction have the assets that they are obligated to deliver at the time of settlement, securities settlement is straightforward. But if the securities settlement system is faced with a set of transactions, in which some party fails to meet their obligation, some transactions will fail to settle. Since the receiving party of a transaction that fails to settle may have been depending on the assets from that transaction in order to meet their own obligations, a single settlement fail can have a ripple effect causing many other transactions to fail to settle. Securities settlement optimization is the problem of finding the optimal set of transactions to settle, given the available assets. In this thesis, we model securities settlement optimization as an integer linear programming problem and evaluate how an optimization solver software solution performs in comparison to a greedy heuristic algorithm on problem instances derived from real settlement data. We find that the solver offers a significant advantage over the heuristic algorithm in terms of reducing settlement fails at the cost of longer execution time. Furthermore, we find that even if the solver is only allowed to run for a couple of minutes it offers a significant advantage compared to the heuristic algorithm. Our conclusion is that using this type of solver for securities settlement optimization seems to be a viable approach, but that further experimentation with other data sets is needed.
Många har bedrivit handel med aktier eller andra värdepapper, men få är medvetna om hur transaktionerna genomförs. Processen där ägandet av värdepapper överförs, ofta i utbyte mot kontanter, kallas värdepappersavveckling. Om parterna som är involverade i transaktionen har de tillgångar som de är skyldiga att leverera vid tidpunkten för avvecklingen är värdepappersavvecklingen enkel. Men om värdepappersavvecklingssystemet står inför en uppsättning transaktioner, där någon part saknar den tillgång som hon är skyldig att leverera, kommer avvecklandet att misslyckas. Eftersom den mottagande parten av en transaktion som inte genomförs kan ha varit beroende av tillgångarna från den transaktionen för att uppfylla sina egna förpliktelser, kan en misslyckad avveckling leda till att många andra transaktioner inte kan genomföras. Värdepappersavveckling bör därmed optimeras för att minimera skadan i en situation där en eller flera transaktioner inta kan genomföras. I det här examensarbetet modellerar vi optimering av värdepappersavveckling som ett linjärprogrammeringsproblem med heltalsvillkor och utvärderar hur en optimeringsprogramvara presterar i jämförelse med en girig heuristisk algoritm på probleminstanser härledda från verklig avvecklingsdata. Vi finner att programvaran erbjuder en betydande fördel jämfört med den heuristiska algoritmen när det gäller att minimera antalet transaktioner som misslyckas, men att det görs till kostnaden av längre exekveringstid. Dessutom upptäcker vi att även om programvaran bara får köra i några minuter ger den en betydande fördel jämfört med den heuristiska algoritmen. Vår slutsats är att användningen av denna typ av programvara för optimering av värdepappersavveckling verkar vara en genomförbar strategi, men att ytterligare experiment med andra dataset behövs.
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Sim, Melvyn 1971. "Robust optimization." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17725.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004.
Includes bibliographical references (p. 169-171).
We propose new methodologies in robust optimization that promise greater tractability, both theoretically and practically than the classical robust framework. We cover a broad range of mathematical optimization problems, including linear optimization (LP), quadratic constrained quadratic optimization (QCQP), general conic optimization including second order cone programming (SOCP) and semidefinite optimization (SDP), mixed integer optimization (MIP), network flows and 0 - 1 discrete optimization. Our approach allows the modeler to vary the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations, while keeping the problem tractable. Specifically, for LP, MIP, SOCP, SDP, our approaches retain the same complexity class as the original model. The robust QCQP becomes a SOCP, which is computationally as attractive as the nominal problem. In network flows, we propose an algorithm for solving the robust minimum cost flow problem in polynomial number of nominal minimum cost flow problems in a modified network. For 0 - 1 discrete optimization problem with cost uncertainty, the robust counterpart of a polynomially solvable 0 - 1 discrete optimization problem remains polynomially solvable and the robust counterpart of an NP-hard o-approximable 0-1 discrete optimization problem, remains a-approximable.
(cont.) Under an ellipsoidal uncertainty set, we show that the robust problem retains the complexity of the nominal problem when the data is uncorrelated and identically distributed. For uncorrelated, but not identically distributed data, we propose an approximation method that solves the robust problem within arbitrary accuracy. We also propose a Frank-Wolfe type algorithm for this case, which we prove converges to a locally optimal solution, and in computational experiments is remarkably effective.
by Melvyn Sim.
Ph.D.
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Lopes, David Granja. "Collections Optimization." Master's thesis, Instituto Superior de Economia e Gestão, 2013. http://hdl.handle.net/10400.5/6505.

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Mestrado em Decisão Económica e Empresarial
Este trabalho debruça-se sobre a otimização do transporte de componentes automóveis. Começa-se por fazer uma revisão bibliográfica e de seguida um enquadramento da situação a estudar, bem como da empresa onde será aplicado (Volkswagen Autoeuropa). É desenvolvido um modelo matemático que permite identificar as rotas ótimas. O objetivo principal deste trabalho é a identificação de rotas otimizadas que permitam o aumento da eficiência, económica e operacional, da cadeia de abastecimento da Volkswagen Autoeuropa. Os resultados foram bastantes promissores, pois foi possível obter uma poupança média de 37% nas rotas identificadas.
This work focuses on optimizing the transport of automotive components. Starts by a literature review and then a framework of the situation being studied, as well as the company where it will be applied (Volkswagen Autoeuropa). A mathematical model that identifies the optimal routes is developed. The main objective of this work is the identification of optimal routes which can increase efficiency, economic and operational, of the supply chain of Wolkswagen Autoeuropa. The results were very promising as it was achieved an average saving of 37% on the identified routes.
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Bylund, Johanna. "Collateral Optimization." Thesis, Umeå universitet, Institutionen för fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-148006.

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Yu, Hao. "Run-time optimization of adaptive irregular applications." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/1285.

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Compared to traditional compile-time optimization, run-time optimization could offer significant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploit the characteristics of input data, programs' dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs' input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs' dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics (collected and analyzed dynamically) of the algorithm's input data, In this dissertation, we specialize this framework for automatic selection of reduction algorithms. In this research, we have identified a small set of machine independent high-level characterization parameters and then we deployed an off-line, systematic experiment process to generate prediction models. These models, in turn, match the parameters to the best optimization transformations for a given machine. The technique has been evaluated thoroughly in terms of applications, platforms, and programs' dynamic behaviors. Specifically, for the reduction algorithm selection, the selected performance is within 2% of optimal performance and on average is 60% better than "Replicated Buffer," the default parallel reduction algorithm specified by OpenMP standard. To reduce the overhead of speculative run-time parallelization, we have developed an adaptive run-time parallelization technique that dynamically chooses effcient shadow structures to record a program's dynamic memory access patterns for parallelization. This technique complements the original speculative run-time parallelization technique, the LRPD test, in parallelizing loops with sparse memory accesses. The techniques presented in this dissertation have been implemented in an optimizing research compiler and can be viewed as effective building blocks for comprehensive run-time optimization systems, e.g., feedback-directed optimization systems and dynamic compilation systems.
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Arayapan, Khanittha, and Piyanut Warunyuwong. "Logistics Optimization: Application of Optimization Modeling in Inbound Logistics." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-6213.

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To be a market leader, low cost and responsiveness are the key success factors. Logistics activities create high cost reducing competitiveness of the company, especially for the remote production base. Thus, logistics activities which are delivery planning, freight forwarder and delivery mode selection must be optimized. The focusing area of this paper is inbound logistics due to its big proportion in the total cost and involvement with several stakeholders. The optimization theory and Microsoft Excel’s Solver is used to create the standard optimization tools since it is an efficient and user friendly program. The models are developed based on the supply chain management theory in order to achieve the lowest cost, responsiveness and shared objectives. 2 delivery planning optimization models, container loading for fixed slitting and loading pattern and container loading for pallet loaded material, are formulated. Also, delivery mode selection is constructed by using optimization concept to determine the best alternative. Furthermore, freight forwarder selection process is created by extending the use of the delivery mode selection model. The results express that safety stock, loading pattern, transport mode, and minimum order quantity (MOQ) significantly affect the total logistics cost. Including hidden costs, long transit time and delay penalties, leads freight forwarder selection process to become more realistic and reliable. Shorter processing time, ensured optimal solution, transparency increase and better communication are gained by using these optimization models. However, the proper boundaries must be defined carefully to gain the feasible solution.

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GOUCHER, DANIEL. "Database optimization : An investigation of code-based optimization techniques." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-153754.

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In this report database optimization is investigated with a focus on the area which can be implemented with software rather than hardware.The reason for this specialization is that software optimization is an area that all developers have the resources to implement.To investigate these optimization techniques I have first presented the theory behind different optimization techniques including indices, caching and materialized views. A brief explanation on how these techniques can be implemented in MySQL and PHP is then given. With the help of these theories a set of tests have been designed to see how the different optimization techniques react to different types of queries and datasizes. The results of these tests are then presented with a discussion on how these results coincide with the theories presented and how they can be used to benefit a specific system.
Databasoptimering - En utredning av kodbaseradeoptimeringstekniker. I den här rapporten utreds databasoptimering med en inriktning på området som kan implementeras med mjukvara. Anledningen till denna inriktning är att mjukvaruoptimering är att område som alla utvecklare har resurserna att implementera.För att undersöka dessa optimeringstekniker så har jag först presenterat teorin bakom olika optimeringstekniker så som index, cachning och materialiserade vyer. En kort beskrivning på hur dessa tekniker kan implementeras i MySQL och PHP ges sedan. Med hjälp av dessa teorier så har en mängd tester skapats för att se hur olika optimeringstekniker reagerar på olika databasfrågor och storlekar. Resultatet av dessa tester presenteras sedan tillsammans med en diskussion på hur de sammanhänger med teorierna som presenterades och hur de kan användas i ett specifikt system.
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Aggarwal, Varun. "Analog circuit optimization using evolutionary algorithms and convex optimization." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40525.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 83-88).
In this thesis, we analyze state-of-art techniques for analog circuit sizing and compare them on various metrics. We ascertain that a methodology which improves the accuracy of sizing without increasing the run time or the designer effort is a contribution. We argue that the accuracy of geometric programming can be improved without adversely influencing the run time or increasing the designer's effort. This is facilitated by decomposition of geometric programming modeling into two steps, which decouples accuracy of models and run-time of geometric programming. We design a new algorithm for producing accurate posynomial models for MOS transistor parameters, which is the first step of the decomposition. The new algorithm can generate posynomial models with variable number of terms and real-valued exponents. The algorithm is a hybrid of a genetic algorithm and a convex optimization technique. We study the performance of the algorithm on artificially created benchmark problems. We show that the accuracy of posynomial models of MOS parameters is improved by a considerable amount by using the new algorithm. The new posynomial modeling algorithm can be used in any application of geometric programming and is not limited to MOS parameter modeling. In the last chapter, we discuss various ideas to improve the state-of-art in circuit sizing.
by Varun Aggarwal.
S.M.
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Books on the topic "Optimization"

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Lange, Kenneth. Optimization. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5838-8.

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Pearce, Charles, and Emma Hunt, eds. Optimization. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98096-6.

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Nguyen, Van Hien, Jean-Jacques Strodiot, and Patricia Tossings, eds. Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57014-8.

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Polak, Elijah. Optimization. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0663-7.

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Miller, Ronald E. Optimization. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1999. http://dx.doi.org/10.1002/9781118032930.

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Lange, Kenneth. Optimization. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4182-7.

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Sharer, Elizabeth, and Hari Rajagopalan. Optimization. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908082.

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Hua, Luogene. Optimization. New York; London: Wiley, 1985.

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Open University. Optimization Course Team., ed. Optimization. Milton Keynes: Open University, 2002.

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University, Open, ed. Optimization. Milton Keynes: Open University, 2002.

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Book chapters on the topic "Optimization"

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Marwedel, Peter. "Optimization." In Embedded Systems, 349–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60910-8_7.

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AbstractEmbedded systems have to be efficient (at least) with respect to the objectives considered in this book. In particular, this applies to resource-constrained mobile systems, including sensor networks embedded in the Internet of Things. In order to achieve this goal, many optimizations have been developed. Only a small subset of those can be mentioned in this book. In this chapter, we will present a selected set of such optimizations. This chapter is structured as follows: first of all, we will present some high-level optimization techniques, which could precede compilation of source code or could be integrated into it. We will then describe concurrency management for tasks. Section 7.3 comprises advanced compilation techniques. The final Sect. 7.4 introduces power and thermal management techniques.
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Serovajsky, Simon. "Additions." In Optimization, 36–66. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-2.

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Serovajsky, Simon. "Counterexamples of optimal control problems with a fixed final state." In Optimization, 313–47. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-11.

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Serovajsky, Simon. "Different counterexamples for optimization problems with isoperimetric conditions." In Optimization, 433–60. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-15.

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Serovajsky, Simon. "Alternative methods." In Optimization, 104–41. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-4.

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Serovajsky, Simon. "Absence of sufficiency and uniqueness in problems with isoperimetric conditions." In Optimization, 405–32. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-14.

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Serovajsky, Simon. "Ill-posed optimal control problems with a fixed final state." In Optimization, 348–72. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-12.

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Serovajsky, Simon. "Optimization of systems with isoperimetric conditions." In Optimization, 377–404. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-13.

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Serovajsky, Simon. "Different optimal control problems for systems with a free initial state." In Optimization, 489–510. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-17.

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Serovajsky, Simon. "Singular controls." In Optimization, 173–204. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003398585-6.

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Conference papers on the topic "Optimization"

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Krus, Petter, and Johan Andersson. "Optimizing Optimization for Design Optimization." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48803.

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Design optimization is becoming and increasingly important tool for design. In order to have an impact on the product development process it must permeate all levels of the design in such a way that a holistic view is maintained through all stages of the design. One important area is in the case of optimization based on simulation, which generally requires non-gradient methods and as a consequence direct-search methods is a natural choice. The idea in this paper is to use the design optimization approach in the optimization algorithm itself in order to produce an efficient and robust optimization algorithm. The result is a single performance index to measure the effectiveness of an optimization algorithm, and the COMPLEX-RF optimization algorithm, with optimized parameters.
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Tang, Zedong, Maoguo Gong, Fenlong Jiang, Hao Li, and Yue Wu. "Multipopulation Optimization for Multitask Optimization." In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8790234.

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Gu, Xiaoyu, John E. Renaud, Leah M. Ashe, Stephen M. Batill, Amarjit S. Budhiraja, and Lee J. Krajewski. "Decision-Based Collaborative Optimization Under Uncertainty." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/dac-14297.

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Abstract In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg (1996a, 1998) is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming the Hazelrigg framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.
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Jian Jiao, Xianjia Wang, and Liubo Zhang. "Applying particle swarm optimization in multiobjective optimization and hybrid optimization." In 2010 Second International Conference on Computational Intelligence and Natural Computing (CINC). IEEE, 2010. http://dx.doi.org/10.1109/cinc.2010.5643832.

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Akpamukcu, Mehmet, and Abdullah Ate. "Improving Equlibrium Optimization Algorithm Performance with Optimization to Optimization Approach." In 2022 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2022. http://dx.doi.org/10.1109/asyu56188.2022.9925491.

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Clapp, Russell M., and Trevor Mudge. "Optimization." In the working group. New York, New York, USA: ACM Press, 1990. http://dx.doi.org/10.1145/322807.322845.

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Yang, Shiyou. "A Modified Particle Swarm Optimization Algorithm for Engineering Optimizations." In The 5th International Conference on Computer Engineering and Networks. Trieste, Italy: Sissa Medialab, 2015. http://dx.doi.org/10.22323/1.259.0004.

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Guo, Zhendong, Wei Sun, Liming Song, Jun Li, and Zhenping Feng. "Generative Transfer Optimization for Aerodynamic Design." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-225.

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Transfer optimization, one type of optimization methods, which leverages knowledge of the completed tasks to accelerate the design progress of a new task, has been in widespread use in machine learning community. However, when applying transfer optimization to accelerate the progress of aerodynamic shape optimization (ASO), two challenges are encountered in sequence, that is, (1) how to build a shared design space among the related aerodynamic design tasks, and (2) how to exchange information between tasks most efficiently. To address the first challenge, a datadriven generative model is used to learn airfoil representations from the existing database, with the aim of synthesizing various airfoil shapes in a shared design space. To address the second challenge, both singleand multifidelity Gaussian processes (GPs) are employed to carry out optimization. On one hand, the multifidelity GP is used to leverage knowledge from the completed tasks. On the other hand, mutual learning is established between singleand multifidelity GP models by exchanging information between them in each optimization cycle. With the above, a generative transfer optimization (GTO) framework is proposed to shorten the design cycle of aerodynamic design. Through airfoil optimizations at different working conditions, the effectiveness of the proposed GTO framework is demonstrated.
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Mukesh, R., K. Lingadurai, and S. Karthick. "Aerodynamic optimization using proficient optimization algorithms." In 2012 International Conference on Computing, Communication and Applications (ICCCA). IEEE, 2012. http://dx.doi.org/10.1109/iccca.2012.6179183.

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SOBIESZCZANSKI-SOBIESKI, J., B. JAMES, and M. RILEY. "Structural optimization by generalized, multilevel optimization." In 26th Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1985. http://dx.doi.org/10.2514/6.1985-697.

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Reports on the topic "Optimization"

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Dunlavy, Daniel M., and Dianne P. O'Leary. Homotopy optimization methods for global optimization. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/876373.

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Zirilli, Francesco, and Luciano Misici. Numerical Optimization. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada198586.

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Bilbro, Griff. Quantum Optimization. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada384604.

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Wenren, Yonghu, Joon Lim, Luke Allen, Robert Haehnel, and Ian Dettwiler. Helicopter rotor blade planform optimization using parametric design and multi-objective genetic algorithm. Engineer Research and Development Center (U.S.), December 2022. http://dx.doi.org/10.21079/11681/46261.

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In this paper, an automated framework is presented to perform helicopter rotor blade planform optimization. This framework contains three elements, Dakota, ParBlade, and RCAS. These elements are integrated into an environment control tool, Galaxy Simulation Builder, which is used to carry out the optimization. The main objective of this work is to conduct rotor performance design optimizations for forward flight and hover. The blade design variables manipulated by ParBlade are twist, sweep, and anhedral. The multi-objective genetic algorithm method is used in this study to search for the optimum blade design; the optimization objective is to minimize the rotor power required. Following design parameter substitution, ParBlade generates the modified blade shape and updates the rotor blade properties in the RCAS script before running RCAS. After the RCAS simulations are complete, the desired performance metrics (objectives and constraints) are extracted and returned to the Dakota optimizer. Demonstrative optimization case studies were conducted using a UH-60A main rotor as the base case. Rotor power in hover and forward flight, at advance ratio 𝜇𝜇 = 0.3, are used as objective functions. The results of this study show improvement in rotor power of 6.13% and 8.52% in hover and an advance ratio of 0.3, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 12.42% reduction at 𝜇𝜇 = 0.4.
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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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Weiland, Lisa M. Nanoscale Transport Optimization. Fort Belvoir, VA: Defense Technical Information Center, December 2008. http://dx.doi.org/10.21236/ada500838.

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Gearhart, Jared Lee, Kristin Lynn Adair, Katherine A. Jones, Alisa Bandlow, Justin David Durfee, Dean A. Jones, Nathaniel Martin, Richard Joseph Detry, Alan Stewart Nanco, and Linda Karen Nozick. Contingency contractor optimization. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1104778.

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Hahn, H. RHIC Luminosity Optimization. Office of Scientific and Technical Information (OSTI), October 1986. http://dx.doi.org/10.2172/1119051.

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Youker, A. J., P. Chung, E. Krahn, and G. F. Vandegrift. Column Optimization Studies. Office of Scientific and Technical Information (OSTI), June 2013. http://dx.doi.org/10.2172/1131388.

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Gearhart, Jared Lee, Kristin Lynn Adair, Katherine A. Jones, Alisa Bandlow, Richard Joseph Detry, Justin David Durfee, Dean A. Jones, Nathaniel Martin, Alan Stewart Nanco, and Linda Karen Nozick. Contingency contractor optimization. Office of Scientific and Technical Information (OSTI), June 2013. http://dx.doi.org/10.2172/1090228.

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