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
wiktionarypar 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 disambig ar bg ca Optimitzaci cs Optimalizace da Optimering es Optimizaci n eu Hoberenatze fr Optimisation ko hr Optimizacija it Ottimizzazione ... 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 ... of the transformed function math f x 1 cdot g x math . Applications of global optimization Typical examples of global optimization applications include Protein structure prediction minimize the energy ... conjecture The starting point of several molecular dynamics simulations consists of an initial optimization .... Branch and bound methods Interval Optimization Interval Algebra see interalg from OpenOpt , Maple global optimization toolbox, TOMLAB for Matlab or GlobSol Methods based on real algebraic geometry Stochastic, thermodynamics Main page Stochastic optimization Several Monte Carlo based algorithms ... algorithms and evolution strategies Swarm intelligence Swarm based optimization algorithms e.g., particle 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 C LGPL a global optimization solver for bound and linear ... 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
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
optimization is a subset of mathematical optimization that is related to operations research , algorithm ...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 ... , and software engineering . Some research literature ref cite web title Discrete Optimization url http www.elsevier.com locate disopt publisher Elsevier accessdate 2009 06 08 ref considers discrete 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 ... 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 problems ... complete discrete optimization problems, current research literature includes the following topics ... index.html author Bill Cook accessdate 2009 06 08 ref . Combinatorial optimization problems can ... time . Since some discrete optimization problems are NP complete , such as the travelling ... applying standard combinatorial optimization algorithms to this problem, one would usually treat the goal ... last Schrijver authorlink Alexander Schrijver title Combinatorial Optimization Polyhedra and Efficiency .... http homepages.cwi.nl lex files histco.pdf On the history of combinatorial optimization till 1960 .... Source code http sourceforge.net projects jcop Java Combinatorial Optimization Platform open ... Optimization February 1, 2006 A. Schrijver William J. Cook, William H. Cunningham, William R. Pulleyblank, Alexander Schrijver Combinatorial Optimization John Wiley & Sons 1 edition November ... more details
Made Easy http www.aimms.com operations research mathematical programming robust optimization ... Tal, A., Nemirovski, A. 1998 . Robust Convex Optimization. Mathematics of Operations Research 23 ... 1 , 5 36. Chen, W. and M. Sim. 2009 . Goal Driven Optimization. Operations Research. 57 2 , 342 357 ... Optimization Perspective on Stochastic Programming. Operations Research, 55 6 , 1058 1071. Dembo, R. 1991 . Scenario optimization, Annals of Operations Research, 30 1 , 63 80. Gupta, S.K. and Rosenhead ... 1995 . Robust Optimization of Large Scale Systems Operations Research, 43 2 ,264 281. Rosenblat, M.J. ...Robust optimization is a field of optimization mathematics optimization theory that deals with optimization ... of the problem. History The origins of robust optimization date back to the establishment ... developments in fields such as operations research , control theory , statistics , economics , and more ... math . What makes this a robust optimization problem is the math forall c,d in P math clause in the constraints ... optimization problem itself is a linear programming problem for each math c,d in P ... of classification criteria for robust optimization problems models. In particular, one can distinguish ... probabilistic models of robustness. Modern robust optimization deals primarily with non probabilistic ... math x math . Global robustness Consider the simple abstract robust optimization problem math max ... of math u math under consideration. This is a global robust optimization problem in the sense that the robustness ... the following robust optimization problem math max x in X, Y subseteq U size Y g x,u le b, forall ... optimization problems that it induces are usually not always very difficult to solve. Example Consider the robust optimization problem math z U max x in X f x g x,u le b, forall u in U math ... of math U math representing normal values of math u math and consider the following robust optimization ... reduces back to the original robustness constraint. This yields the following relaxed robust optimization ... 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 ... 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 ... faster at performing addition and Loop computing Loops loop ing operations than multiplication and division. Visible anchor Trade offs Pessimization redirects here Optimization will generally focus ... clarity and conciseness. There are instances where the programmer performing the optimization must decide to make the software better for some operations but at the cost of making other operations less ... more details
. ref Operations research Another field that uses optimization techniques extensively is operations ... Thomas Magnanti year 1977 publisher Addison Wesley cite book title Optimization in operations research ... Infrastructure for Operations Research http plato.asu.edu guide.html Decision Tree for Optimization ... optimization Category Operations research ar az Optimalla d rma bn ...other uses Optimization disambiguation File MaximumParaboloid.png right thumb The maximum mathematics ... optimization alternatively, optimization or mathematical programming refers to the selection ... Glossary , INFORMS Computing Society. ref In the simplest case, an optimization problem consists ... mathematics value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics . More generally, optimization includes .... Optimization problems main Optimization problem An optimization problem can be represented in the following ... an optimization problem or a mathematical programming problem a term not directly related to computer ... an optimal solution . By convention, the standard form of an optimization problem is stated ... global optimization . Notation Optimization problems are often expressed with special notation. Here .... Historically, the first term for optimization was linear programming , which was due to George Dantzig .... Later important researchers in mathematical optimization include the following col begin col 2 Richard ... squares, which is an optimization method. Major subfields Convex programming studies the case ... is a subfield of convex optimization where the underlying variables are semidefinite matrix mathematics ... term, this is a type of convex programming. Fractional programming studies optimization ... to a convex optimization problem. Nonlinear programming studies the general case in which the objective ... s. Robust optimization Robust programming is, like stochastic programming, an attempt to capture ... 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 ... 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 ... 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 ... 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 ... issue 4598 ref Reactive Search Optimization reactive search optimization RSO by Roberto Battiti ... and Intelligent Optimization last Battiti first Roberto authorlink coauthors Mauro Brunato Franco ... approach for global optimization of complex potential energy landscapes journal Phys. Rev. Lett ... Cite book author Goldberg, D. E. title Genetic Algorithms in Search, Optimization, and Machine ... strategies See also Global optimization Machine learning Gaussian process State Space Model ... operations research mathematical programming stochastic programming AIMMS AIMMS http www.optirisk ... http www.dash optimization.com home products products sp.html XPRESS SP DEFAULTSORT Stochastic Optimization Category Estimation theory Category Probability theory Category Stochastic optimization it Ottimizzazione ... more details
Traffic Optimization are the methods by which time stopped is reduced. Need for traffic optimization Texas Transportation Institute estimates travel delays of 220,000,000 hours all over the U.S. and between 17 55 hours of delay per person in 2005 ref http mobility.tamu.edu ums congestion data tables national table 6.pdf ref relating to congestion on the streets. Traffic device optimization hence becomes a significant aspect of operations. Techniques Several techniques exist to reduce delay of traffic. Generally the algorithms attempt to reduce delays user time , stops, emissions, or some other measure of effectiveness. Many optimization software are geared towards pretimed coordinated systems. Real time traffic control Several systems are capable of monitoring the traffic arrivals and adjusting timings based on the detected inputs. Traffic Detectors may range from Metal Detectors to Detectors that use Image Detection. Metal detectors are the most popular in use. Image detection devices exhibit numerous problems including degradation during bad weather and lighting. Traffic actuated signal systems use detectors to adjust timing for Only the main street semi actuated system Both main and cross streets fully actuated system. Criticism It has been suggested that the benefits of traffic optimization have never been scientifically justified. It inherently favors motorized traffic over alternate modes such as pedestrians, bicyclists, and transit users and may promote more auto use. ref Michael J. Vandeman, http home.pacbell.net mjvande synch4 Is Traffic Signal Synchronization Justifiable? , April 15, 1994 ref It is suggested that an alternate approach could involve traffic calming , and a conceptual focus on the movement of people and goods rather than vehicles. See also Intelligent Transportation System Intelligent Traffic Systems References Reflist External links http www.highways.gov.uk ... www.scats.com.au SCATS Sydney Coordinated Adaptive Traffic System DEFAULTSORT Traffic Optimization ... 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
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
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
In compiler theory , peephole optimization is a kind of optimization computer science optimization performed over a very small set of instructions in a segment of generated code. The set is called a peephole or a window . It works by recognising sets of instructions that don t actually do anything, or that can be replaced by a leaner set of instructions. Replacement rules disputed section Replacement rules date December 2010 Common techniques applied in peephole optimization ref Crafting a Compiler with C , Fischer LeBlanc ref Constant folding Evaluate constant subexpressions in advance. Strength reduction Replace slow operations with faster equivalents. Null sequences Delete useless operations Combine Operations Replace several operations with one equivalent. Algebraic Laws Use algebraic laws to simplify or reorder instructions. Special Case Instructions Use instructions designed for special operand cases. Address Mode Operations Use address modes to simplify code. There can, of course, be other types of peephole optimizations involving simplifying the target machine instructions, assuming that the target machine is known in advance. Advantages of a given architecture and instruction sets can be exploited in this case. Examples Replacing slow instructions with faster ones The following Java bytecode ... aload 1 aload 1 mul ... can be replaced by ... aload 1 dup mul ... This kind of optimization, like most peephole optimizations, makes certain assumptions about the efficiency of instructions. For instance, in this case, it is assumed that the code dup code operation which duplicates and pushes the top of the stack data structure stack is more efficient than the code aload X code operation which loads a local Variable programming variable identified as code X code and pushes ... registers is generally redundant. In cases where it is redundant, a peephole optimization ... Corporation produced the COBOL program optimization optimizer , an early mainframe object code optimizer ... more details
to dynamically improve optimization. Strength reduction Replace complex or difficult or expensive operations ...No footnotes date April 2009 Compiler optimization is the process of tuning the output of a compiler ... for minimizing the Energy conservation power consumed by a program. Compiler optimization is generally ... been shown that some code optimization problems are NP complete , or even undecidable problem undecidable ... its task place upper limits on the optimizations that a compiler implementor might provide. Optimization ... these factors, optimization rarely produces optimal output in any sense, and in fact an optimization ... in typical programs. Types of optimizations Techniques used in optimization can be broken up among .... Some examples of scopes include Peephole optimization s Usually performed late in the compilation process after machine code has been generated. This form of optimization examines a few adjacent ... about them is available . Loop optimization s These act on the statements which make up a loop, such as a for loop ... program optimization These analyze all of a program s source code. The greater quantity of information ... to local information i.e., within a single function . This kind of optimization can also allow new ... is replaced by a copy of the function body. Machine code optimization These analyze the executable ... Optimization Improving Executable Object Code author Clinton F. Goss date June 1986 publisher location ... general categories of optimization Programming language independent vs language dependent Most ... optimization techniques can be used across languages. However, certain language features make some ... platform. The following is an instance of a local machine dependent optimization. To set a Processor ... affecting optimization The machine itself Many of the choices about which optimizations can and should ... exceptions, there are usually fewer combinations of registers and memory operations, and the instruction ... that require it. Common themes To a large extent, compiler optimization techniques have the following ... more details
GNU Free Documentation License . Image IO All Up Model.png frame Optimization All up Model Infrastructure optimization is Microsoft s structured, systematic process for assessing an organization s IT infrastructure and application platform across capabilities in order to provide an optimization roadmap toward a Dynamic IT. The roadmap helps companies to define and implement optimization initiatives ... Partner . 1 February 2008. ref These optimization initiatives also enhance user needs and user experience in order to increase productivity and amplify the impact of employees. Optimization enables ... three Optimization models based on industry and analyst work. The Optimization models are vendor agnostic ... ReferenceA Optimization can be viewed in three perspectives ref name ReferenceA Barney, Doug. http redmondmag.com features article.asp?editorialsid 2394 Infrastructure Optimization for IT . Redmond . 1 January 2008. ref Core Infrastructure Business Productivity Infrastructure Application Platform Optimization Models The Microsoft Optimization models have been developed using industry best practice ... the models is to develop a simple way to use an Optimization framework that is flexible and can ... . Each Optimization model includes specific technical capabilities that provide a comprehensive set of solutions to help advance a customer s infrastructure and platform Optimization levels. Core Infrastructure Optimization Model br The Core IO model, considered the most developed of the three models ... Optimization model structures sales efforts . Directions on Microsoft . 25 June 2007 ref helps ... Infrastructure Optimization Model br The BPIO model, introduced in 2007, ref McLaughlin, Kevin. http www.crn.com showArticle.jhtml?articleID 202101586 Microsoft infrastructure optimization campaign .... Application Platform Optimization Model br The APO model enables organizations to drive the business ... Optimization Model The APO model defines five capabilities that are necessary to build a more ... 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
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
In mathematical optimization , ordinal optimization is the maximization of functions taking values in a partially ordered set poset . Ordinal optimization has applications in the theory of queuing theory queuing flow network networks . Mathematical foundations See also Mathematical optimization Partially ordered set Lattice Greedoid Antimatroid Combinatorial optimization Duality mathematics Order reversing dualities Ordinal optimization is the maximization of function taking values in a partially ... that L is a join semilattice the second says that L is a meet semilattice . Both operations are monotone ... structure . ref Fujishige, Satoru Submodular functions and optimization . Second edition. Annals ... . Operations Research Computer Science Interfaces Series, 41. Springer, New York, 2008. xx ... optimization in ordered algebraic structures . Ann. Discrete Math. 10 1981 , viii 380 ... optimizationoptimization with multiple objectives . ref cite book last Z linescu first C. title ... River Edge, NJ, 2002 pages xx 367 isbn 981 238 067 1 mr 1921556 ref Ordinal optimization in computer science and statistics See also Selection algorithm Problems of ordinal optimization arise in many ... Discrete event simulation Since the 1960s, the field of ordinal optimization has expanded in theory ... 8, 0 691 11763 2, 0 691 11763 2 mr 2188299 ref See also Stochastic optimization Computational complexity ... colwidth 30em Further reading Fujishige, Satoru Submodular functions and optimization . Second edition ... models and algorithms . Operations Research Computer Science Interfaces Series, 41. Springer, New York .... Linear and combinatorial optimization in ordered algebraic structures . Ann. Discrete Math. 10 1981 ... Ho Ho, Y.C. , Sreenivas, R., Vakili, P., Ordinal Optimization of Discrete Event Dynamic Systems , J ... on ordinal optimization by Yu Chi Ho DEFAULTSORT Ordinal Optimization Category Mathematical optimization Category Control theory Category Order theory Category Selection algorithms Category Optimization ... 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 ..., 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 ... representation is also used for other purposes besides finding objects with graduated optimization ... james l 1981 1.pdf pn date October 2011 ref Graduated optimization can be used in manifold learning. The Manifold Sculpting algorithm, for example, uses graduated optimization to seek a manifold ... first4 AN title Graduated optimization of fractionation using a 2 component model volume 30 issue ... matching and graduated optimization year 2003 last1 Ming Ye last2 Haralick first2 R.M. last3 Shapiro ... 12 pages 1625 30 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 ... a similar effect. fact date October 2011 References Reflist DEFAULTSORT Graduated Optimization Category ... 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
Random optimization 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, random optimization, is attributed to Matyas ref name matyas65random who made an early presentation of RO along with basic mathematical analysis. RO works by iteratively moving to better positions in the search space which are sampled using e.g. a normal distribution surrounding the current position. Algorithm Let f Unicode & x211D sup n sup Unicode & x211D be the fitness or cost function which must be minimized. Let x Unicode & x211D sup n sup designate a position or candidate solution in the search space. The basic ... to begin with. See also Random search is a closely related family of optimization methods ... 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 Random optimization journal Automation and Remote Control year 1965 volume 26 number 2 ... optimization method for constrained optimization problems journal Journal of Optimization Theory ... cite journal last1 Dorea first1 C.C.Y. title Expected number of steps of a random optimization method journal Journal of Optimization Theory and Applications year 1983 volume 39 number 3 pages ... of the Baba and Dorea random optimization methods journal Journal of Optimization Theory and Applications ... Major subfields of optimization DEFAULTSORT Random Optimization Category Optimization algorithms and methods Category Mathematical optimization fr Optimisation al atoire ... 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
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