xyz

xyz 軟體王

會員登錄
xyz 站內搜索 購物結帳 手動下單 問題反應 訂單查詢 訂購說明
您現在的位置:網站首頁 程式軟體光碟 掃描、PDF、辦公文書工具 碟片詳情
商品编号: PDF2090
商品名稱: Gurobi Optimization Gurobi v4.5.1
語系版本: 英文正式版
商品類型: 智慧引擎提供了新一代的高精確性的描繪軟體
運行平台: WindowsXP/Vista/7
更新日期: 2011-07-15
碟片數量: 1片
銷售價格: 100
瀏覽次數: 1771
熱門標籤: Gurobi  Optimization 

轉載TXT文檔】  
您可能感興趣: xp 
您可能也喜歡:
PDF6646--Qtrac XindeX v1.3.0 Win64
PDF6643--Qtrac diffpdfc v5.6.7 Win64
PDF6648--PentaLogix ViewMate Pro v11.12.9
PDF6655--MetaProducts Offline Explorer v7.4.4579 SR2
PDF6663--IDM Ultraedit v24.0.0.42 x86/x64
Gurobi Optimization Gurobi v4.5.1
Gurobi Optimization Gurobi v4.5.1 英文正式版(智慧引擎提供了新一代的高精確性的描繪軟體)


破解說明:
關掉主程式,破解檔放置於crack夾內,請將破解檔複製於主程式的安裝目錄內既可破解
內容說明:
Gurobi智慧引擎提供了新一代的高精確性的描繪方案。
英文說明:
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.
圖片說明:

相關商品:
  • Wiley RFID for the Optimization of Business Processes Apr 2008 英文正式版(PDF格式電子書)


  • 站內搜索
         
    購物清單
    熱門關鍵字
    程薇   
    上學期   
    李祥   
    周易   
    莊柏   
    xyz藍光   
    xyz志光   
    點線面   
    KO會考   
    口袋書   
    李居明   
    占卜   
    李宗瑞   
    book   
    97年   
    林晟   
    馬蓋先   
    SPSS   
    SolidWorks   
    Engineer   
    flash   
    譯典通   
    正航   
    迪士尼   
    微軟   
    陳巃羽   
    高昇   
    建宏   
    金安   
    全都會   
    Siemens   
    Microsoft   
    幼教   
    巧連智   
    iPhone   
    Android   
    sitemap 知識 軟體王 wiki xyz xyz xyz xyz xyz