《线性混合整数优化》(Gurobi Optimization Gurobi)v4.0.1[压缩包]
![《线性混合整数优化》(Gurobi Optimization Gurobi)v4.0.1[压缩包]](/Uploads/33/337/337990.jpg)
电驴资源
| 用户共享的电驴资源列表 | |
|---|---|
| [求解线性规划].Gurobi.Optimization.Gurobi.v4.0.1.Cracked-EAT.zip | 7.19 MB |
| 7.19 MB | |
电驴资源简介
中文名: 线性混合整数优化英文名: Gurobi Optimization Gurobi资源格式: 压缩包版本: v4.0.1发行时间: 2010年制作发行: Gurobi Optimization地区: 美国语言: 英文简介:
/thumb.jpg)
/thumb.jpg)
软件类型：行业软件-整数优化
软件性质：破解软件
操作系统：Windows
应用平台：Winll
问题反馈： http://www.gurobi.com/html/contact.html
网站链接： http://www.gurobi.com/
软件介绍：
Gurobi 4 隆重发布，在数学优化器领域继续扩大领先优势。主要特色包括：
新增 QP 和 MIQP 优化器；
在版本3基础上，线性和混合整数问题求解速度进一步提升；
数值计算稳定性进一步提升；
并发 LP 计算；
新增 MIP 终止计算策略选项；
支持和 Visual Studio 2010 集成
Java 和 .Net 环境下浮动许可的更多自主控制。
Gurobi 特点
Gurobi 具有许多独特的特点和功能，可以使得用户迅速而准确地获得最优结果。这些特点包括：
采用最新优化技术，充分利用多核处理器优势
任何版本都支持并行计算，并且计算结果确定而非随机
提供了方便轻巧的接口，支持 C++, Java, Python, .Net 开发，内存消耗少
支持多种平台，包括 Windows, Linux, Mac OS X
支持 AMPL、GAMS、AIMMS和 Windows Solver Foundation 建模环境
单一版本，开发版本也就是发布版本，程序转移便捷
性价比突出，为学校、企业提供了差异化价格，方便各种需求
第三方商业和免费软件支持和Matlab接口
强大的技术支持力量，Gurobi 提供中英文双语技术支持
完备的用户使用手册
Gurobi 可以解决的问题
Gurobi 可以解决的数学问题：
线性问题（Linear problems）
二次型目标问题（Quadratic problems）
混合整数线性和二次型问题（Mixed integer linear and quadratic problems）
突出的性价比
Gurobi 不区分开发许可和实施许可，一个许可软件既可以用在开发上也可以用在实施上。同时，允许一个许可软件应用于多个应用程序上，极大地降低了大型优化项目的开发和实施成本。
应用领域
线性混合整数优化是应用在各个领域中最常见的优化方法之一，是过去30年当中在实际应用中创造价值最巨大的优化方法。在物流、生产制造、金融、交通运输、资源管理、集成电路设计、环境保护、电力管理等等领域，几乎无所不在。在世界一流的企业资源管理（ERP）、供应链管理（SCM）、运输管理等企业决策工具中，都有线性混合整数优化器的存在。
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.
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
更多资讯请点击访问
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
本资源下载链接来自ShareVirus
代码 ± SINCE 2000 ±
E A T P R E S E N T S
Gurobi.Optimization.Gurobi.v4.0.1.Cracked-EAT
±²² ²²±
²²°² RELEASE INFO ²°²²
° ± ± °
° ± 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
²²°² RELEASE NOTES ²°²²
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 ... -
±²² ²²±
²²°² INSTALLATION NOTES ²°²²
±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.
本资源帖谢绝任何广告及与资源无关的评论,凡涉及广告内容的评论一概删除处理!
软件类型：行业软件-整数优化
软件性质：破解软件
操作系统：Windows
应用平台：Winll
问题反馈： http://www.gurobi.com/html/contact.html
网站链接： http://www.gurobi.com/
软件介绍：
Gurobi 4 隆重发布，在数学优化器领域继续扩大领先优势。主要特色包括：
新增 QP 和 MIQP 优化器；
在版本3基础上，线性和混合整数问题求解速度进一步提升；
数值计算稳定性进一步提升；
并发 LP 计算；
新增 MIP 终止计算策略选项；
支持和 Visual Studio 2010 集成
Java 和 .Net 环境下浮动许可的更多自主控制。
Gurobi 特点
Gurobi 具有许多独特的特点和功能，可以使得用户迅速而准确地获得最优结果。这些特点包括：
采用最新优化技术，充分利用多核处理器优势
任何版本都支持并行计算，并且计算结果确定而非随机
提供了方便轻巧的接口，支持 C++, Java, Python, .Net 开发，内存消耗少
支持多种平台，包括 Windows, Linux, Mac OS X
支持 AMPL、GAMS、AIMMS和 Windows Solver Foundation 建模环境
单一版本，开发版本也就是发布版本，程序转移便捷
性价比突出，为学校、企业提供了差异化价格，方便各种需求
第三方商业和免费软件支持和Matlab接口
强大的技术支持力量，Gurobi 提供中英文双语技术支持
完备的用户使用手册
Gurobi 可以解决的问题
Gurobi 可以解决的数学问题：
线性问题（Linear problems）
二次型目标问题（Quadratic problems）
混合整数线性和二次型问题（Mixed integer linear and quadratic problems）
突出的性价比
Gurobi 不区分开发许可和实施许可，一个许可软件既可以用在开发上也可以用在实施上。同时，允许一个许可软件应用于多个应用程序上，极大地降低了大型优化项目的开发和实施成本。
应用领域
线性混合整数优化是应用在各个领域中最常见的优化方法之一，是过去30年当中在实际应用中创造价值最巨大的优化方法。在物流、生产制造、金融、交通运输、资源管理、集成电路设计、环境保护、电力管理等等领域，几乎无所不在。在世界一流的企业资源管理（ERP）、供应链管理（SCM）、运输管理等企业决策工具中，都有线性混合整数优化器的存在。
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.
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
更多资讯请点击访问
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
本资源下载链接来自ShareVirus
代码 ± SINCE 2000 ±
E A T P R E S E N T S
Gurobi.Optimization.Gurobi.v4.0.1.Cracked-EAT
±²² ²²±
²²°² RELEASE INFO ²°²²
° ± ± °
° ± 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
²²°² RELEASE NOTES ²°²²
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 ... -
±²² ²²±
²²°² INSTALLATION NOTES ²°²²
±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.
本资源帖谢绝任何广告及与资源无关的评论,凡涉及广告内容的评论一概删除处理!