中文名: 線性混合整數優化
英文名: Gurobi Optimization Gurobi
資源格式: 壓縮包
版本: v4.0.1
發行時間: 2010年
制作發行: Gurobi Optimization
地區: 美國
語言: 英文
簡介:


軟件類型：行業軟件-整數優化
軟件性質：破解軟件
操作系統：Windows
應用平台：Winll
問題反饋： http://www.gurobi.com/html/contact.html
網站鏈接： http://www.gurobi.com/
軟件介紹：
Gurobi 4 隆重發布，在數學優化器領域繼續擴大領先優勢。主要特色包括：
新增 QP 和 MIQP 優化器；
在版本3基礎上，線性和混合整數問題求解速度進一步提升；
數值計算穩定性進一步提升；
並發 LP 計算；
新增 MIP 終止計算策略選項；
支持和 Visual Studio 2010 集成
Java 和 .Net 環境下浮動許可的更多自主控制。
Gurobi 特點
Gurobi 具有許多獨特的特點和功能，可以使得用戶迅速而准確地獲得最優結果。這些特點包括：
采用最新優化技術，充分利用多核處理器優勢
任何版本都支持並行計算，並且計算結果確定而非隨機
提供了方便輕巧的接口，支持 C++, Java, Python, .Net 開發，內存消耗少
支持多種平台，包括 Windows, Linux, Mac OS X
支持 AMPL、GAMS、AIMMS和 Windows Solver Foundation 建模環境
單一版本，開發版本也就是發布版本，程序轉移便捷
性價比突出，為學校、企業提供了差異化價格，方便各種需求
第三方商業和免費軟件支持和Matlab接口
強大的技術支持力量，Gurobi 提供中英文雙語技術支持
完備的用戶使用手冊
Gurobi 可以解決的問題
　
Gurobi 可以解決的數學問題：
線性問題（Linear problems）
二次型目標問題（Quadratic problems）
混合整數線性和二次型問題（Mixed integer linear and quadratic problems）
突出的性價比
Gurobi 不區分開發許可和實施許可，一個許可軟件既可以用在開發上也可以用在實施上。同時，允許一個許可軟件應用於多個應用程序上，極大地降低了大型優化項目的開發和實施成本。
應用領域
線性混合整數優化是應用在各個領域中最常見的優化方法之一，是過去30年當中在實際應用中創造價值最巨大的優化方法。在物流、生產制造、金融、交通運輸、資源管理、集成電路設計、環境保護、電力管理等等領域，幾乎無所不在。在世界一流的企業資源管理（ERP）、供應鏈管理（SCM）、運輸管理等企業決策工具中，都有線性混合整數優化器的存在。
The Gurobi Optimizer
The Gurobi Optimizer is a state-of-the-art solver for linear programming (LP), quadratic programming (QP) and mixed-integer programming (MILP and MIQP). It was designed from the ground up to exploit modern multi-core processors.
For solving LP and QP models, the Gurobi Optimizer includes high-performance implementations of the primal simplex method, the dual simplex method, and a parallel barrier solver. For MILP and MIQP models, the Gurobi Optimizer incorporates the latest methods including cutting planes and powerful solution heuristics. All models benefit from advanced presolve methods to simplify models and slash solve times.
Every Gurobi license allows parallel processing, and the Gurobi Parallel Optimizer is deterministic: two separate runs on the same model will produce identical solution paths.
The Gurobi Optimizer is written in C and is accessible from several languages. In addition to a powerful, interactive Python interface and a matrix-oriented C interface, we provide object-oriented interfaces from C++, Java, Python, and the .NET languages. These interfaces have all been designed to be lightweight and easy to use, with the goal of greatly enhancing the accessibility of our products. And since the interfaces are lightweight, they are faster and use less memory than other standard interfaces. Our online documentation (Quick Start Guide, Example Tour and Reference Manual) describes the use of these interfaces.
The Gurobi Optimizer is available for popular computing platforms including Microsoft Windows, Linux and Mac OS X; a full list is available in the Platforms table.
Accessing the Gurobi Optimizer
We offer commercial licenses that support a variety of usage scenarios. You can purchase licenses for a single system, floating licenses that allow several users on a network to use Gurobi, and licenses for embedding Gurobi inside your product. Pricing information can be found in our client area; please Login and select 'Pricing' on the left for additional information. To purchase a license, please click here for contact information.
A free Trial version is available for immediate download and installation. This version will accept problems with up to 500 variables and 500 constraints. With the exception of these size restrictions, this version is full featured, including access to all Gurobi solvers and interfaces.
We also offer free Academic Licenses to faculty, students, and staff at qualifying academic institutions. These licenses have no size restrictions. They provide complete access to the full set of features of our commercial product.
We also offer Gurobi Cloud, which allows you to use the Gurobi Optimizer on an hourly basis via the Amazon Elastic Computing Cloud (EC2).
The Gurobi solvers are also available through a number of powerful third-party modeling systems. Gurobi Optimization is an authorized reseller for two of these systems: AMPL and Microsoft Solver Foundation. For AMPL, please Login to the Gurobi client area and select 'Pricing' on the left for additional information on purchasing this systems through us. For more information about Microsoft Solver Foundation, please send email to info[at]gurobi.com.
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±‏ SINCE 2000 ‏±
 
‏ E A T P R E S E N T S ‏
Gurobi.Optimization.Gurobi.v4.0.1.Cracked-EAT
‏ ‏
   
±²²   ²²±
²²°²  RELEASE INFO  ²°²²
° ± ± °
° ± SUPPLIER ....: TEAM EAT ± °
 ± PROG TYPE ...: SCIENTIFIC ± 
 ° LANGUAGE ....: ENGLISH ° 
 RELEASE DATE.: 2011-01-28 
 ° ° 
 ° CRACKER ......: TEAM EAT ° 
 PROTECTION ...: DEMO-LIMITS 
 DIFFICULTY ...: GUESS! 
 
 PACKAGER ....: TEAM EAT 
 FORMAT ......: ZIP/RAR 
 ARCHIVE NAME.: eatgb40a.zip 
 No OF DISKS .: [XX/02] 
 
 REQUIREMENTS .: WinXP/Vista/Win7 
 PRICE ........: $8,500.00 
 WEBSITE.......: http://www.gurobi.com 
 
²²°²  RELEASE NOTES  ²°²²
 
 The Gurobi Optimizer is a state-of-the-art solver 
 for linear programming (LP), quadratic programming 
 (QP) and mixed-integer programming (MILP and MIQP). 
 It was designed from the ground up to exploit modern 
 multi-core processors. Every Gurobi license allows 
 parallel processing, and the Gurobi Parallel 
 Optimizer is deterministic: two separate runs on the 
 same model will produce identical solution paths. 
 
 For solving LP and QP models, the Gurobi Optimizer 
 includes high-performance implementations of the 
 primal simplex method, the dual simplex method, and 
 a parallel barrier solver. For MILP and MIQP models, 
 the Gurobi Optimizer incorporates the latest methods 
 including cutting planes and powerful solution 
 heuristics. All models benefit from advanced 
 presolve methods to simplify models and slash solve 
 times. 
 
 The Gurobi Optimizer is written in C and is 
 accessible from several languages. In addition to a 
 powerful, interactive Python interface and a 
 matrix-oriented C interface, we provide 
 object-oriented interfaces from C++, Java, Python, 
 and the .NET languages. These interfaces have all 
 been designed to be lightweight and easy to use, 
 with the goal of greatly enhancing the accessibility 
 of our products. And since the interfaces are 
 lightweight, they are faster and use less memory 
 than other standard interfaces. Our online 
 documentation (Quick Start Guide, Example Tour and 
 Reference Manual) describes the use of these 
 interfaces. 
 
 Gurobi is also available through several powerful 
 third-party modeling systems including AIMMS, AMPL, 
 FRONTLINE SOLVERS, GAMS, MPL, OptimJ and TOMLAB. 
 
 Version 4.0 of the Gurobi Optimizer includes a 
 number of enhancements. Users of previous versions 
 may need to make a few minor changes to their 
 existing programs. Here are the new features, and 
 the likely changes required to existing programs. 
 
 New features: 
 * Quadratic programming: The Gurobi Optimizer now 
 supports models with quadratic objective 
 functions. The new version includes primal 
 simplex, dual simplex, and parallel barrier 
 optimizers for continuous QP models, and a 
 parallel branch-and-cut solver for Mixed Integer 
 Quadratic Programming (MIQP) models. 
 * Concurrent optimizer: The Gurobi Optimizer now 
 allows you to run multiple algorithms 
 simultaneously when solving a linear continuous 
 model on a multi-core machine. The optimizer 
 returns when the first algorithm solves the model. 
 We include both a standard concurrent optimizer 
 and a deterministic concurrent optimizer. The 
 latter returns the exact same solution every time 
 you run it, while the former can sometimes return 
 different optimal solutions from one run to the 
 next. The former can sometimes be significantly 
 faster. 
 * MIP performance: The MIP solver is faster in 
 release 4.0. These improvements do not require any 
 parameter changes. 
 * LP performance: The simplex and barrier solvers 
 are slightly faster in release 4.0. We have also 
 improved the numerical stability of the primal 
 simplex solver and the barrier crossover 
 algorithm. 
 * Delayed MIP strategy change: The Gurobi Optimizer 
 now gives you the option to change a few MIP 
 parameters in the middle of the optimization in 
 order to dynamically shift the search strategy. 
 Specifically, two new parameters, ImproveStartGap 
 and ImproveStartTime, allow you to specify when 
 the algorithm should modify the values of a few 
 parameters that control the intensity of the MIP 
 heuristics. By setting one or both of these 
 parameters to non-default values, you can cause 
 the MIP solver to switch from its standard 
 parameter settings, where it tries to strike a 
 balance between finding better solutions and 
 proving that the current solution is optimal, to a 
 set of parameter values that focus almost entirely 
 on finding better solutions. 
 * Support for Visual Studio 2010: Gurobi Optimizer 
 now supports Microsoft Visual Studio 2010. This 
 change only affects C++ users, who will find new 
 libraries gurobi_c++2010md.lib, 
 gurobi_c++2010mdd.lib, gurobi_c++2010mt.lib, and 
 gurobi_c++2010mtd.lib in the lib directory of the 
 Gurobi distribution. 
 * Explicit license release in Java and .NET: The new 
 version includes an explicit method for releasing 
 a Gurobi license. You no longer need to rely on 
 the garbage collector to reclaim unused licenses. 
 * New methods, attributes, parameters, and error 
 codes: To support the new features in Gurobi 4.0, 
 we have added several new methods, attributes, 
 parameters, and error codes. You can learn more 
 about these in the {Gurobi Reference Manual}. 
 
 New methods: 
 * New C methods for managing Q: the C interface 
 includes three new routines, GRBaddqpterms, 
 GRBdelq, and GRBgetq. These allow you to add, 
 delete, and retrieve quadratic objective terms, 
 respectively. 
 * Quadratic expressions: the object oriented 
 interfaces include a new quadratic expression 
 class, GRBQuadExpr, which can be used to build 
 quadratic objective functions. 
 * getObjective/setObjective: the object oriented 
 interfaces include new GRBModel methods that allow 
 you to retrieve the current objective function as 
 a linear or quadratic expression, and allow you to 
 set the objective equal to a linear or quadratic 
 expression. 
 * getValue: the object oriented interfaces include a 
 new getValue method that allows you to compute the 
 value of a GRBLinExpr or GRBQuadExpr object for 
 the current solution. 
 * License release: the Java and .NET interfaces 
 include a new release method that allows you to 
 release the license held by an environment 
 immediately, instead of having to wait for the 
 garbage collector to reclaim the GRBEnv object. 
 
 New attributes: 
 * IsQP: model attribute that indicates whether the 
 model has any quadratic terms. 
 * NumQNZs: model attribute that returns the number 
 of quadratic terms in the objective function. 
 
 New parameters: 
 * Method: the previous LPMethod parameter has been 
 renamed to Method. This new parameter controls the 
 algorithm used to solve continuous linear and 
 quadratic models. We have added two new options: 
 concurrent and deterministic concurrent. This 
 parameter also selects the algorithm used to solve 
 the root node of a MIP model. 
 * NodeMethod: chooses the algorithm used to solve 
 node relaxations in a MIP model. 
 * ModKCuts: controls the generation of mod-k cuts. 
 * ImproveStartGap: allows you to specify the 
 optimality gap at which the MIP solver resets a 
 few MIP heuristics parameters in order to shift 
 the attention of the MIP solver to finding the 
 best possible feasible solution. 
 * ImproveStartTime: allows you to specify the 
 elapsed time at which the MIP solver resets a few 
 MIP heuristics parameters in order to shift the 
 attention of the MIP solver to finding the best 
 possible feasible solution. 
 * PreMIQPMethod: chooses the presolve transformation 
 performed on MIQP models. 
 * PSDTol: sets a limit on the amount of diagonal 
 perturbation that the optimizer is allowed to do 
 on the Q matrix for a quadratic model. If a larger 
 perturbation is required, the optimizer will 
 terminate with an GRB_ERROR_Q_NOT_PSD error. 
 
 New error codes: 
 * GRB_ERROR_Q_NOT_PSD: This new error code is 
 returned when you attempt to solve a QP model 
 where the Q matrix is not positive semi-definite 
 (meaning there exists an x for which x'Qx ). Note 
 that the optimizer will always try to add a small 
 perturbation to the diagonal to correct small PSD 
 violations. This error will be reported when the 
 required perturbation is too large (as controlled 
 by the new PSDTol parameter). 
 
° ²° COMMENTS °² °
 Do NOT distribute this release outside of the scene 
 Keep the scene alive and secure! 
 
All good progs start as freeware,
then things get worse ...;-)
±²²   ²²±
²²°²  INSTALLATION NOTES  ²°²²
±² ² ‏ `TLB' ‏ ² ²±
± ± Try it, Like it, Buy it! ± ±
° ° ° 
 1. Unpack and install. 
 2. Copy the included files over the originals. 
 
 That's all. Have fun using it!;-) 
 
 ___________________________________________________________________ 
 
 Always remember to block applications (or go off line) from calling 
 home 'during install'. Once installed, disable 'check for automatic 
 updates' option if available, so that you don't get it blacklisted. 
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

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