Randomoptimization RO is a family of numerical Optimization mathematics optimization methods that do not require the gradient of the problem to be optimized and RO can hence be used on functions that are not Continuous function continuous or differentiable . Such optimization methods are also known as direct search, derivative free, or black box methods. The name, randomoptimization, is attributed ... to begin with. See also Random search is a closely related family of optimization methods ... cite journal last1 Dorea first1 C.C.Y. title Expected number of steps of a randomoptimization method journal Journal of Optimization Theory and Applications year 1983 volume 39 number 3 pages ... of the Baba and Dorea randomoptimization methods journal Journal of Optimization Theory and Applications ... Major subfields of optimization DEFAULTSORT RandomOptimization Category Optimization algorithms and methods Category Mathematical optimization fr Optimisation al atoire ... RO algorithm can then be described as Initialize x with a random position in the search space ... random vector to the current position x If f y f x then move to the new position by setting ... that purely random sampling of the search space will inevitably yield a sample arbitrarily close to the optimum ... optimization method using a Uniform distribution continuous uniform distribution in its sampling and a simple formula for exponentially decreasing the sampling range. Pattern search optimization Pattern .... Stochastic optimization References reflist refs ref name matyas65random cite journal last Matyas first J. title Randomoptimization journal Automation and Remote Control year 1965 volume 26 number 2 pages 246 253 ref ref name baba81convergence cite journal last Baba first N. title Convergence of a randomoptimization method for constrained optimization problems journal Journal of Optimization Theory ... last1 Solis first1 F.J. last2 Wets first2 R.J B. title Minimization by random search techniques ... more details
Global optimization is a branch of applied mathematics and numerical analysis that deals with the optimization mathematics optimization of a function mathematics function or a Set mathematics set of functions ... nonlinear optimization problems, the objective function math f math has a large number of local ... often leads to very hard challenges. Applications of global optimization Typical examples of global optimization applications include Protein structure prediction minimize the energy free energy ... simulations consists of an initial optimization of the energy of the system to be simulated. Spin ... page Stochastic optimization Several Monte Carlo based algorithms exist Simulated annealing Direct ... Swarm intelligence Swarm based optimization algorithms e.g., particle swarm optimization , Multi swarm optimization and ant colony optimization Memetic algorithm s, combining global and local search strategies Reactive search optimization i.e. integration of sub symbolic machine learning techniques into search heuristics Differential evolution Graduated optimization Response surface methodology based approaches Efficient Global Optimization IOSO Indirect Optimization based on Self Organization Global optimization software 1. Free and opensource class wikitable Name Source code br language License Brief info http ab initio.mit.edu nlopt NLopt C LGPL free open source optimization library with several global optimization algorithms http www.ra.cs.uni tuebingen.de software EvA2 EvA2 Java LGPL an extensive open source Java framework for global optimization OpenOpt Python programming language Python BSD licenses BSD Universal cross platform numerical optimization framework, br see its http openopt.org GLP global optimization page and http openopt.org Problems other problems involved http www.norg.uminho.pt aivaz pswarm PSwarm GlobSol C LGPL a global optimization solver for bound and linear ... OptimizatioN http archimedes.cheme.cmu.edu baron baron.html BARON http www.pinterconsulting.com ... more details
Optimization software can refer to software for Category Computer system optimization software The optimization of computer systems Category Mathematical optimization software Mathematical optimization disambig Category Application software ... more details
Graduated optimization is a global optimization technique that attempts to solve a difficult optimization ... while optimizing until it is equivalent to the difficult optimization problem. ref cite book ... LOCAL COPIES BMVA96Tut node29.html chapter Graduated Non Convexity and Multi Resolution Optimization Methods title Vision Through Optimization year 1996 ref ref cite book first1 Andrew last1 Blake ... right thump 200px An illustration of graduated optimization. Graduated optimization is an improvement ... a difficult optimization problem into a sequence of optimization problems, such that the first problem ... point to the next problem in the sequence, and the last problem in the sequence is the difficult optimization problem that it ultimately seeks to solve. Often, graduated optimization gives better results ... solution to the final problem in the sequence. These conditions are The first optimization problem ..., it can be difficult to find a sequence of optimization problems that meet these conditions. Often, graduated optimization yields good results even when the sequence of problems cannot be proven to strictly meet all of these conditions. Some examples Graduated optimization is commonly used ... CVPR , June 2012. ref . Graduated optimization can be used in manifold learning. The Manifold Sculpting algorithm, for example, uses graduated optimization to seek a manifold embedding for non linear ... Graduated optimization of fractionation using a 2 component model volume 30 issue 2 pages 131 5 journal ... optimization year 2003 last1 Ming Ye last2 Haralick first2 R.M. last3 Shapiro first3 L.G. journal ... ref and other purposes. Related optimization techniques Simulated annealing is closely related to graduated optimization. Instead of smoothing the function over which it is optimizing, simulated annealing .... fact date October 2011 Because simulated annealing relies on random sampling to find improvements, however ..., graduated optimization smooths the function being optimized, so local optimization techniques ... more details
about iterative method s the modeling and optimization of decisions under uncertainty stochastic programming Stochastic optimization SO methods are optimization mathematics optimization iterative method method s that generate and use random variable s. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involve random objective function s or random constraints, for example. Stochastic optimization methods also include methods with random iterates. Some stochastic optimization methods use random iterates to solve stochastic problems, combining both meanings of stochastic optimization. ref name spall2003 Cite book author Spall, J. C. title Introduction to Stochastic Search and Optimization year 2003 publisher Wiley url http www.jhuapl.edu ISSO isbn 0471330523 ref Stochastic optimization methods generalize deterministic system mathematics deterministic methods for deterministic problems. Methods for stochastic functions Partly random input data arise in such areas as real time estimation and control, simulation based optimization ... fu2002 cite journal author Fu, M. C. title Optimization for Simulation Theory vs. Practice journal ... random error in the measurements of the criterion. In such cases, knowledge that the function values are contaminated by random noise leads naturally to algorithms that use statistics statistical ... performance uniformly across many data sets, for many sorts of problems. Stochastic optimization ... ref name kirk1983 cite journal author S. Kirkpatrick coauthors C. D. Gelatt M. P. Vecchi title Optimization ... Probability Collectives in Optimization year 2011 booktitle Handbook of Statistics editor C.R. Rao ... ref Reactive Search Optimization reactive search optimization RSO by Roberto Battiti , G. Tecchiolli ... Optimization last Battiti first Roberto authorlink coauthors Mauro Brunato Franco Mascia ... The Cross Entropy Method year 2004 publisher Springer Verlag isbn 978 0387212401 ref random search ... more details
In applied mathematics and theoretical computer science , combinatorial optimization synonymous or subfield? discrete optimization citation needed is a topic that consists of finding an optimal object .... It operates on the domain of those optimization problems, in which the set of Candidate ... is to find the best solution. Some common problems involving combinatorial optimization are the traveling ... optimization is a subset of mathematical optimization that is related to operations research ... , and software engineering . Some research literature ref cite web title Discrete Optimization url ... optimization to consist of integer programming together with combinatorial optimization which in turn is composed of optimization problem s dealing with Graph mathematics graphs , matroid s, and related ... on polynomial time algorithm s for certain special classes of discrete optimization, a considerable amount of it unified by the theory of linear programming . Some examples of combinatorial optimization ... complete discrete optimization problems, current research literature includes the following topics ... tractable algorithms that perform well on random instances e.g. for Traveling salesman problem TSP path length for random pointset in a square TSP approximation algorithm s that run in polynomial time ... optimal index.html author Bill Cook accessdate 2009 06 08 ref . Combinatorial optimization problems ... in polynomial time . Since some discrete optimization problems are NP complete , such as the traveling ... standard combinatorial optimization algorithms to this problem, one would usually treat the goal ... Schrijver title Combinatorial Optimization Polyhedra and Efficiency publisher Springer series Algorithms ... of combinatorial optimization till 1960 . Lecture notes http people.brunel.ac.uk mastjjb jeb or ip.html ... Optimization Platform open source project. Others Alexander Schrijver http homepages.cwi.nl lex files dict.pdf A Course in Combinatorial Optimization February 1, 2006 A. Schrijver William ... more details
Continuous optimization is a branch of Optimization mathematics optimization in applied mathematics . As opposed to discrete optimization , the Variable mathematics variables used in the Optimization mathematics objective function can assume real number real values, e.g., values from intervals of the real line. Category Mathematical optimization Mathapplied stub ... more details
Orphan date October 2009 The scenario approach or scenario optimization approach is a technique for obtaining solutions to robust optimization and chance constrained optimization problems based on randomization of the constraints. The technique has existed for decades as a heuristic approach, but the field has been given a systematic theoretical foundation only recently thanks to some important achievements by Marco Campi, Giuseppe Calafiore, Simone Garatti and Maria Prandini. Description In Optimization mathematics optimization , robustness features translate into constraints that are parameterized in the uncertain elements of the problem. The scenario method ref G. Calafiore and M.C. Campi. Uncertain Convex Programs randomized Solutions and Confidence Levels. Mathematical Programming, 102 25&ndash 46, 2005. http www.springerlink.com content qlcbr9eg3dne6ldb ?p ad759ab4ef9f49049f9b5ef14e7b00ee&pi 1 ref ref M.C. Campi and S. Garatti. The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs. SIAM J. on Optimization, 19, no.3 1211&ndash 1230, 2008. http siamdl.aip.org ...&key DISPLAY&docID 1&page 1&chapter 0 ref simply consists in extracting at random some instances ... level the use of randomization in robust and chance constrained optimization. Deleted image removed Image risk return.jpg thumb Figure 1 risk return trade off When the constraints are convex optimization ... Feasibility and Optimality . Available on Optimization Online. http www.optimization online.org ... robust the various solutions are. The final outcome is a risk robustness vs. return optimization value ... and solve the scenario optimization program math max x min i 1, dots,N R delta i x . 1 math .... Addendum Why this example can be seen as an optimization program with uncertain constraints? To better relate this investment problem to the previous discussion where optimization problems with uncertain ... references Category Stochastic optimization Category Decision theory Category Control theory ... more details
For algorithms to solve other optimization problems Optimization mathematics Mergefrom Algorithmic efficiency Optimization techniques date September 2009 In computer science , program optimization or software optimization is the process of modifying a software system to make some aspect of it work more ... or other resources, or draw less power. General Although the word optimization shares the same root as optimal , it is rare for the process of optimization to produce a truly optimal system. The optimized ... so the process of optimization may be halted before a completely optimal solution has been reached ... of optimizationOptimization can occur at a number of levels Design level At the highest level ... be decided, arguments against early or premature optimization may be hard to justify. In some cases, however, optimization relies on using more elaborate algorithms, making use of special cases ... has a cost removed because out of context, random and interrupts logical progression of argument ... itself generates, and few projects need resort to this ultimate optimization step. However, a large ... phase run time optimization exceeding the capability of static compilers by dynamically adjusting parameters ... Code optimization can be also broadly categorized as computer platform platform dependent and platform ... level optimization, platform independent techniques are generic techniques such as loop unrolling, reduction ..., data level parallelism, cache optimization techniques i.e., parameters that differ among various ... The optimization, sometimes performed automatically by an optimizing compiler, is to select a method ... can often be achieved by removing extraneous functionality. Optimization is not always ... and division. Visible anchor Trade offs Pessimization redirects here Optimization will generally focus ... include code clarity and conciseness. There are instances where the programmer performing the optimization ... the code. Bottlenecks Optimization may include finding a Bottleneck engineering bottleneck ... more details
some of the constraints or parameters depend on random variable s. Robust optimization Robust programming is, like stochastic programming, an attempt to capture uncertainty in the data underlying the optimization problem. This is not done through the use of random variables, but instead, the problem ...other uses Optimization disambiguation File MaximumParaboloid.png right thumb Graph of a paraboloid given ... , computational science , or management science , mathematical optimization alternatively, optimization or mathematical programming refers to the selection of a best element from some set .... ref In the simplest case, an optimization problem consists of maxima and minima maximizing or minimizing .... The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics . More generally, optimization includes finding best available values ... types of objective functions and different types of domains. Optimization problems main Optimization problem An optimization problem can be represented in the following way Given a function mathematics ... 0 sub f x for all x in A maximization . Such a formulation is called an optimization problem or a mathematical ..., the standard form of an optimization problem is stated in terms of minimization. Generally ... to the actual optimal solution of a non convex problem is called global optimization . Notation Optimization ... proposed iterative methods for moving towards an optimum. Historically, the first term for optimization ... optimization include the following col begin col 2 Richard Bellman Ronald A. Howard Narendra Karmarkar ... did some stuff here , Gauss developed the method of least squares, which is an optimization method ... of quadratic programs. Semidefinite programming SDP is a subfield of convex optimization where ... programming. Fractional programming studies optimization of ratios of two nonlinear functions. The special class of concave fractional programs can be transformed to a convex optimization problem. Nonlinear ... more details
Search optimization may refer to Search algorithm Search engine Search engine optimization disambig Long comment to avoid being listed on short pages ... more details
Engineering Optimization ref S. S. Rao, Engineering Optimization Theory and Practice, Wiley, 2009 ref ref X. S. Yang, Engineering Optimization An Introduction with Metaheuristic Applications, Wiley, 2010. ref is the subject which uses optimization techniques to achieve design goals in engineering . ref J. N. Siddall, Optimal Engineering Design, CRC Press, 1982 . ref It is also called design optimization. Its topics include structural design e.g., pressure vessel design, welded beam design , shape optimization , topological optimization e.g., airfoil , inverse optimization, processing planning, product designs and others. References Reflist Category Engineering concepts ... more details
Computer optimization may mean Solving an optimization mathematics optimization problem using a computer . Optimizing the performance of a computer system via Category Computer hardware tuning hardware tuning and or adjusting some operating system related settings either directly or using a piece of Category Computer system optimization software computer system optimization software . e.g., using disk defragmentation software. dab ... more details
Citations missing date March 2008 Expert subject mathematics date April 2009 Discrete optimization is a branch of Optimization mathematics optimization in applied mathematics and computer science . As opposed to continuous optimization , the Variable mathematics variables used in the optimization mathematics mathematical program or some of them are restricted to assume only discrete mathematics discrete values, such as the integers. Two notable branches of discrete optimization are combinatorial optimization , which refers to problems on graph mathematics graph s, matroid s and other discrete structures integer programming These branches are closely intertwined however since many combinatorial optimization problems can be modeled as integer programs e.g. Shortest path Linear programming formulation shortest path and conversely, integer programs can often be given a combinatorial interpretation. Category Mathematical optimization mathapplied stub eo Diskreta optimumigo pl Programowanie ca kowitoliczbowe ru zh ... more details
Unreferenced date December 2009 Demand optimization is the application of processes and tools to maximize return on sales . This usually involves the application of mathematical modeling techniques using computer software. It has particular applications in retail , where merchants wish to identify the best combination of price and promotion marketing promotion to achieve desired sales, gross margin , inventory or market share objectives. The methods used are similar to those applied in the related field of supply chain optimization , where mathematical algorithms are applied to large databases of sales data to help Forecasting predict future outcomes . In the case of demand optimization, as well as in house sales history, there may be competitive pricing information. Because it is still a new field, authoritative data on the benefits of demand optimization is not widely available, although suppliers offer case studies of early adopters which claim rapid return on investment , especially in the optimization of the timing and level of price markdown s. See also Demand shortfall Price Profit maximization Yield management Price discrimination DEFAULTSORT Demand Optimization Category Pricing Category Mathematical optimization ... more details
Product optimization is the practice of making changes or adjustments to a product to make it more desirable. Description A product has a number of attributes. For example, a soda bottle can have different packaging variations, flavors, nutritional values. It is possible to optimize a product by making minor adjustments. Typically, the goal is to make the product more desirable and to increase marketing metrics such as Purchase Intent, Believability, Frequency of Purchase, etc. Methods Multivariate optimization is one of the most common methods for product optimization. In this method, multiple product attributes are specified and then tested with consumers. Due to complex interaction effects between different attributes for example, consumers frequently associate certain flavors with packaging colors , it is problematic to use mathematical methods, such as Conjoint Analysis, typically used in industrial process optimization. More recently companies started to adopt Evolutionary Optimization techniques for Product optimization. Evolutionary algorithms such as IDDEA are used to optimize products, concepts and messaging. Category Product development ... more details
Orphan date August 2009 Hydrological optimization applies mathematical Optimization mathematics optimization techniques such as linear programming to water related problems. These problems may be for surface water , groundwater , or the combination. The work is interdisciplinary, and may be done by hydrologist s, civil engineer s, environmental engineer s, and operations research ers. Groundwater and surface water flows can be studied with hydrologic simulation . A typical program used for this work is MODFLOW . However, simulation models cannot easily help make management decisions, as simulation is descriptive. Simulation shows what would happen given a certain set of conditions. Optimization, by contrast, finds the best solution for a set of conditions. Optimization models have three parts 1 an objective, such as Minimize cost , 2 decision variables, which correspond to the options available to management, and 3 constraints, which describe the technical or physical requirements imposed on the options. To use hydrological optimization, a simulation is run to find constraint coefficients for the optimization. An engineer or manager can then add costs or benefits associated with a set of possible decisions, and solve the optimization model to find the best solution. Examples of problems solved with hydrological optimization Contaminant remediation in aquifers. The decision problem is where to locate wells, and choose a pumping rate, to minimize the cost to prevent spread of a contaminant. The constraints are associated with the hydrogeological flows. Maximizing well abstraction subject to environmental flow constraints Wagner 1995, Feyen and Gorelick 2005 . The goal is to measure the effects of each user s water use on other users and on the environment, as accurately as possible, and then optimize over the available feasible solutions. Hydrological optimization is now ... Hydrological Optimization Category Hydrology ... more details
wiktionary optimizationOptimization or optimality may refer to Mathematical optimization , the theory and computation of extrema or stationary points of functions Economics and business Optimality, in economics see utility and economic efficiency Pareto efficiency Pareto optimality , or Pareto efficiency, a concept used in economics, game theory, engineering, and the social sciences Process optimization , in business and engineering, methodologies for improving the efficiency of a production process Product optimization , in business and marketing, methodologies for improving the quality and desirability of a product or product concept Information technology Program optimization , improving software to make it work more efficiently or use fewer resources Compiler optimization , improving the performance or efficiency of compiled code Asymptotically optimal algorithm , an algorithm that is at most a constant factor worse than the best possible algorithm for large input sizes Search engine optimization , in internet marketing, methodologies aimed at improving the ranking of a website in search engine listings Image search optimization , in internet marketing, methodologies aimed at improving the ranking of an image in image search engine listings Other Optimality theory , in linguistics, a model proposing that observed forms of language arise from the interaction of conflicting constraints Optimization role playing games , a gaming play style Optimum may refer to Optimum Releasing , a film and DVD distribution company based in the UK Optimum TV , the brand name of a suite of digital media services offered by Cablevision Systems Corporation Optimum PR, a division of Cossette, Inc. , a public relations organization See also Maximization disambiguation Management science Operations research Formal science disambiguation ar bg ca Optimitzaci cs Optimalizace da Optimering es Optimizaci n eu Hoberenatze fr Optimisation ko hr Optimizacija it Ottimizzazione ... more details
No footnotes date October 2011 Conic optimization is a subfield of convex optimization that studies a class of structured convex optimization problems called conic optimization problems. A conic optimization problem consists of minimizing a convex function over the intersection of an affine subspace and a convex cone . The class of conic optimization problems is a subclass of convex optimization problems and it includes some of the most well known classes of convex optimization problems, namely linear programming linear and semidefinite programming . Definition Given a real number real vector space X , a convex function convex , real valued function mathematics function math f C to mathbb R math defined on a convex cone math C subset X math , and an affine subspace math mathcal H math defined by a set of affine constraints math h i x 0 math , a conic optimization problem is to find the point math x math in math C cap mathcal H math for which the number math f x math is smallest. Examples of math C math include the positive semidefinite matrices math mathbb S n math , the positive orthant math x geq mathbf 0 math for math x in mathbb R n math , and the second order cone math left x,t in mathbb R n 1 lVert x rVert leq t right math . Often math f math is a linear function, in which case the conic optimization problem reduces to a semidefinite programming semidefinite program , a linear program , and a second order cone programming second order cone program , respectively. Duality Certain special cases of conic optimization problems have notable closed form expressions of their dual problems. Conic LP The dual of the conic linear program minimize math c T x math subject to math ... math Z geq0 math External links cite book title Convex Optimization first1 Stephen P. last1 Boyd first2 ... MOSEK Software capable of solving conic optimization problems. Category Mathematical optimization Category Convex optimization ... more details
see also Deduplication Capacity optimization is a general term for technologies used to improve storage utilization by shrinking stored data. The primary technologies used for capacity optimization are deduplication and data compression . These solutions are delivered as software or hardware solution, integrated with existing storage systems or delivered as standalone products. Deduplication algorithms look for redundancy in sequences of bytes across comparison windows. Typically using cryptographic hash functions as identifiers of unique sequences, sequences are compared to the history of other such sequences, and where possible, the first uniquely stored version of a sequence is referenced rather than stored again. Different solutions use different methods for selecting data windows, from 4KB blocks to full file comparisons known as Single Instance Storage or SIS. Capacity optimization generally refers to the use of this kind of technology in a storage system. An example of this kind of system is the Venti file system ref http cm.bell labs.com who seanq venti fast02 talk.pdf Venti filesystem ref in the Plan9 open source OS. There are also implementations in networking especially Wide Area networking , where they are sometimes called bandwidth optimization or WAN Optimization technologies. ref http www.cs.washington.edu homes djw papers spring sigcomm00.pdf Spring and Wetherall, A Protocol Independent Technique for Eliminating Redundant Network Traffic ref Commercial implementations of capacity optimization are most often found in backup recovery storage, where storage of iterating versions of backups day to day creates an opportunity for reduction in space using this approach. The term was first used widely in 2005. ref http searchstorage.techtarget.com sDefinition 0,290660,sid5 gci1103991,00.html Capacity optimization defined by searchstorage.com ref References references software eng stub Category Software optimization ... more details
Random search RS is a family of numerical Optimization mathematics optimization methods that do not require the gradient of the problem to be optimized and RS can hence be used on functions that are not Continuous function continuous or differentiable . Such optimization methods are also known as direct search, derivative free, or black box methods. The name, random search, is attributed to Rastrigin ref name rastrigin63convergence who made an early presentation of RS along with basic mathematical analysis. RS works by iteratively moving to better positions in the search space which are sampled from a hypersphere surrounding the current position. Algorithm Let f Unicode & x211D sup n sup ... Randomoptimization is a closely related family of optimization methods which sample from a normal distribution instead of a hypersphere. Luus Jaakola is a closely related optimization method using a Uniform ... can then be described as Initialize x with a random position in the search space. Until a termination ... in the literature Fixed Step Size Random Search FSSRS is Rastrigin s ref name rastrigin63convergence basic algorithm which samples from a hypersphere of fixed radius. Optimum Step Size Random Search OSSRS ... and is therefore expensive to execute. Adaptive Step Size Random Search ASSRS by Schumer and Steiglitz ..., however, is somewhat complicated. Optimized Relative Step Size Random Search ORSSRS by Schrack and Choit ... decreasing the sampling range. Pattern search optimization Pattern search takes steps along ... name rastrigin63convergence cite journal last Rastrigin first L.A. title The convergence of the random ... Schumer first1 M.A. last2 Steiglitz first2 K. title Adaptive step size random search journal IEEE ... random searches journal Mathematical Programming year 1976 volume 10 number 1 pages 230 244 ref Metaheuristics for real valued problems Major subfields of optimization Category Optimization algorithms and methods Category Stochastic optimization ... more details
wikify date July 2010 In cellular communications technology, Self Optimization is a process in which the system s settings are autonomously and continuously adapted to the traffic profile and the network environment in terms of topology, propagation and interference. ref cite web url http www.detecon dmr.com en print.html?unique id 194502 title Control the Chaos last Roberts first Ken coauthors Josef Thormann, Murugaraj Shanmugam publisher Detecon Consulting accessdate 14 July 2010 ref Together with Self Planning and Self Healing, Self Optimization is one of the key pillars of the Self Organizing Networks SON management paradigm proposed by NGMN. ref cite journal last Honglin first Hu coauthors Jian Zhang, et al date February 2010 title Self configuration and self optimization for LTE networks journal IEEE Communications Magazine publisher IEEE Press location Piscataway, NJ volume 42 issue 2 pages 94 100 issn 0163 6804 doi 10.1109 MCOM.2010.5402670 url http portal.acm.org citation.cfm?id 1771767 accessdate 14 July 2010 ref The autonomous trait of Self Optimization involves no human intervention at all during the aforementioned optimization process. References reflist Category 3rd Generation Partnership Project standards ... more details
Logic optimization , a part of logic synthesis , is the process of finding an equivalent representation of the specified logic circuit under one or more specified constraints. Generally the circuit is constrained to minimum chip area meeting a prespecified delay. Introduction With the advent of logic synthesis , one of the biggest challenges faced by the Electronic design automation EDA industry was to find the best netlist representation of the given design description. While two level logic optimization had long existed in the form of the Quine McCluskey algorithm , later followed by the Espresso heuristic logic minimizer , the rapidly improving chip densities, and the wide adoption of Hardware description language HDLs for circuit description, formalized the logic optimization domain as it exists today. Today, logic optimization is divided into various categories based on two criteria Based on circuit representation Two level logic optimization Multi level logic optimization Based on circuit characteristics Sequential logic optimization Combinational logic optimization While a two level circuit representation of circuits strictly refers to the flattened view of the circuit in terms of SOPs Canonical form Boolean algebra sum of products &mdash which is more applicable to a Programmable logic array PLA implementation of the design Clarify date February 2010 &mdash a multi level representation is a more generic view of the circuit in terms of arbitrarily connected SOPs, POSs Canonical form Boolean algebra product of sums , factored form etc. Logic optimization algorithms generally work either on the structural SOPs, factored form or functional BDDs, ADDs representation of the circuit. Clarify date February 2010 Two level versus multi level representations If we have two ... diagram Circuit minimization References Synthesis and Optimization of Digital Circuits , by Giovanni ... Digital electronics Category Electronic design automation Category Electronics optimization ... more details
nofootnotes date December 2011 Process optimization is the discipline of adjusting a process so as to optimize some specified set of parameters without violating some constraint. The most common goals are minimizing cost, maximizing throughput, and or efficiency. This is one of the major quantitative property quantitative tools in industrial decision making. When optimizing a process, the goal is to maximize one or more of the process specifications, while keeping all others within their constraints. Areas Fundamentally, there are three parameters that can be adjusted to affect optimal performance. They are Equipment optimization The first step is to verify that the existing equipment is being used to its fullest advantage by examining operating data to identify equipment bottlenecks. Operating procedures Operating procedures may vary widely from person to person or from shift to shift. Automation of the plant can help significantly. But automation will be of no help if the operators take control and run the plant in manual. Control optimization In a typical processing plant, such as a chemical plant or oil refinery , there are hundreds or even thousands of control loops. Each control loop is responsible for controlling one part of the process, such as maintaining a temperature, level, or flow. If the control loop is not properly designed and tuned, the process runs below its optimum. The process will be more expensive to operate, and equipment will wear out prematurely. For each control loop to run optimally, identification of sensor, valve, and tuning problems is important ... supervision. See also Calculation of glass properties , optimization of several properties Deficit irrigation to optimize water productivity Metallurgical Process Optimization Process simulation External links http www.expertune.com r2.asp?f Wikipedia&l learncast.html Tutorials on Process Optimization ... and graph algorithms. Category Process management Category Mathematical optimization de Prozessoptimierung ... more details
Image Meta Optimization Concept.JPG thumb Meta optimization concept. In numerical Optimization mathematics optimization , meta optimization is the use of one optimization method to tune another optimization method. Meta optimization is reported to have been used as early as in the late 1970s by Mercer ... . Meta optimization is also known in the literature as meta evolution, super optimization, automated ... problems .JPG thumb Performance landscape for differential evolution . Optimization methods such as genetic ... parameters of an optimizer can be varied and the optimization performance plotted as a landscape. This is computationally feasible for optimizers with few behavioural parameters and optimization problems ... is therefore needed to search the space of behavioural parameters. Methods Image DE Meta Optimization Progress 12 benchmark problems .JPG thumb Meta optimization of differential evolution . A simple ... operators were reported by B ck. ref name back94parallel Meta optimization of particle swarm optimization .... ref name birattari02racing ref name birattari04thesis meta optimized ant colony optimization . Statistical ... parameters and optimization performance, see for example Francois and Lavergne, ref name francois01design and Nannen and Eiben. ref name nannen06method A comparison of various meta optimization techniques ... title Optimization of control parameters for genetic algorithms journal IEEE Transactions Systems, Man ... doi 10.1016 0954 1810 95 95751 Q last Keane first A.J. title Genetic algorithm optimization in multi ... Optimization OPSO and its application to artificial neural network training journal BMC Bioinformatics ... pedersen08simplifying.pdf title Simplifying particle swarm optimization journal Applied ... back94parallel cite conference last1 B ck first1 T. title Parallel optimization of evolutionary ... 2006 pages 183 190 ref Category Optimization algorithms and methods Category Evolutionary algorithms Category Heuristics Category Mathematical optimization ... more details
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