Gurobi Optimization Gurobi for AMPL v5.0.1 英文正式版(線性混合整數優化軟體線性混合整數優化軟體)
Gurobi 4 隆重發佈，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；
併發 LP 計算；
新增 MIP 終止計算策略選項；
支援和 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準確地獲得最優結果。這些特點包括：
採用最新優化技術，充分利用多核處理器優勢
任何版本都支持平行計算，並且計算結果確定而非隨機
提供了方便輕巧的介面，支援 C++, Java, Python, .Net 開發，記憶體消耗少
支持多種平臺，包括 Windows, Linux, Mac OS X
支援 AMPL、GAMS、AIMMS和 Windows Solver Foundation 建模環境
單一版本，開發版本也就是發佈版本，程式轉移便捷
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體支援和Matlab介面
強大的技術支援力量，Gurobi 提供中英文雙語技術支援
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，一個許可軟體既可以用在開發上也可以用在實施上。
同時，允許一個許可軟體應用于多個應用程式上，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、生產製造、金融、交通運輸、資源管理、積體
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 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.
Most of the changes in the 4.5 release of the Gurobi
Optimizer are related to performance. Users of
previous versions will typically not need to make
any changes to their programs to use the new
version. The new version does contain a few new
features, described here.
* New default Method for continuous models: The
new version uses a new Automatic setting as the
default for solving continuous models. In previous
releases, continuous models were solved with the
dual simplex method by default. While the exact
strategy used by the new Automatic setting may
change in future releases, in this release the new
approach uses the concurrent optimizer for
continuous models with a linear objective (LPs),
the barrier optimizer for continuous models with a
quadratic objective (QPs), and the dual simplex
optimizer for the root node of a MIP model. You
should change the Method parameter if you would
like to choose a different method.
* New Minimum Releaxation heuristic: The new
version contains a new Minimum Relaxation
heuristic that can be useful for finding solutions
to MIP models where other strategies fail to find
feasible solutions in a reasonable amount of time.
Use the new MinRelNodes parameter to control this
new heuristic.
* New branch direction control: The new version
allows more control over how the branch-and-cut
tree is explored. Specifically, when a node in the
MIP search is completed and two child nodes,
corresponding to the down branch and the up branch
are created, the new BranchDir parameter allows
you to determine whether the MIP solver will
explore the down branch first, the up branch
first, or whether it will choose the next node
based on a heuristic determination of which
sub-tree appears more promising.
* Cut pass limit: The new version allows you to
limit the number of cut passes performed during
root cut generation in MIP. Use the new CutPasses
parameter.
* Additional information for infeasible and
unbounded linear models: The new version allows
you to obtain a Farkas infeasibility proof for
infeasible models, and an unbounded ray for
unbounded models. Use the new InfUnbdInfo
parameter, and the new FarkasProof, FarkasDual,
UnbdRay attributes to obtain this information.
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