Update README.md (#323)

README.md

@@ -8,23 +8,41 @@ The [Gurobi](http://www.gurobi.com) Optimizer is a commercial optimization solve
 
 You need to set the NonConvex parameter:
 ```julia
-model = Model(with_optimizer(Gurobi.Optimizer, NonConvex = 2))
+model = Model(Gurobi.Optimizer)
+set_optimizer_attribute(model, "NonConvex", 2)
 ```
 
 ## Use with JuMP
 
 We highly recommend that you use the *Gurobi.jl* package with higher level packages such as [JuMP.jl](https://github.com/JuliaOpt/JuMP.jl).
 
-This can be done using the ``Gurobi.Optimizer`` object. Here is how to create a *JuMP* model that uses Gurobi as the solver. Parameters are passed as keyword arguments:
+This can be done using the ``Gurobi.Optimizer`` object. Here is how to create a *JuMP* model that uses Gurobi as the solver. 
 ```julia
 using JuMP, Gurobi
 
 model = Model(Gurobi.Optimizer)
-set_optimizer_attribute(model,"TimeLimit",100)
-set_optimizer_attribute(model,"Presolve",0)
+set_optimizer_attribute(model, "TimeLimit", 100)
+set_optimizer_attribute(model, "Presolve", 0)
 ```
 See the [Gurobi Documentation](https://www.gurobi.com/documentation/current/refman/parameters.html) for a list and description of allowable parameters.
 
+## Reusing the same Gurobi environment for multiple solves
+
+When using this package via other packages such as [JuMP.jl](https://github.com/JuliaOpt/JuMP.jl), the default behavior is to obtain a new Gurobi license token every time a model is created and solved. If you are using Gurobi in a setting where the number of concurrent Gurobi uses is limited (e.g. ["Single-Use" or "Floating-Use" licenses](http://www.gurobi.com/products/licensing-pricing/licensing-overview)), you might instead prefer to obtain a single license token that is shared by all models that your program solves. You can do this by passing a Gurobi Environment object as the first parameter to `Gurobi.Optimizer`. For example, the follow code snippet solves multiple problems with JuMP using the same license token:
+
+```julia
+using JuMP, Gurobi
+
+const GRB_ENV = Gurobi.Env()
+
+model1 = Model(() -> Gurobi.Optimizer(GRB_ENV))
+...
+
+# The solvers can have different options too
+model2 = Model(optimizer_with_attributes(() -> Gurobi.Optimizer(GRB_ENV), "OutputFlag" => 0))
+...
+```
+
 ### Common Performance Pitfall with JuMP
 
 Gurobi API works differently than most solvers. Any changes to the model are not applied immediately, but instead go sit in a internal buffer (making any modifications appear to be instantaneous) waiting for a call to `update_model!` (where the work is  done). If Gurobi.jl is used directly, it is the user responsability to call `update_model!` when necessary (for example, before solving the model), as it would be if the user was using the official C interface. However, if Gurobi.jl is used with JuMP, it becomes Gurobi.jl responsibility to call `update_model!` when necessary, as a valid JuMP program should work for solvers with and without such lazy update semantics (i.e., with and without a `update_model!`-like method).
@@ -98,23 +116,6 @@ julia> import Pkg; Pkg.build("Gurobi")
 ```
 The Gurobi library (`gurobiXX.dll` on Windows, `gurobiXX.so` on Unix, and `gurobiXX.dylib` in OSX where `XX` is a version) will be searched for in ``GUROBI_HOME/lib`` on unix platforms and ``GUROBI_HOME\bin`` on Windows.
 
-## Reusing the same Gurobi environment for multiple solves
-
-When using this package via other packages such as [JuMP.jl](https://github.com/JuliaOpt/JuMP.jl), the default behavior is to obtain a new Gurobi license token every time a model is created and solved. If you are using Gurobi in a setting where the number of concurrent Gurobi uses is limited (e.g. ["Single-Use" or "Floating-Use" licenses](http://www.gurobi.com/products/licensing-pricing/licensing-overview)), you might instead prefer to obtain a single license token that is shared by all models that your program solves. You can do this by passing a Gurobi Environment object as the first parameter to `Gurobi.Optimizer`. For example, the follow code snippet solves multiple problems with JuMP using the same license token:
-
-```julia
-using JuMP, Gurobi
-
-const GRB_ENV = Gurobi.Env()
-
-model1 = Model(with_optimizer(Gurobi.Optimizer, GRB_ENV))
-...
-
-# The solvers can have different options too
-model2 = Model(with_optimizer(Gurobi.Optimizer, GRB_ENV, OutputFlag=0))
-...
-```
-
 ## Accessing Gurobi-specific attributes via JuMP
 
 You can get and set Gurobi-specific variable, constraint, and model attributes via JuMP as follows: