Use OptimizationNLPModels from branch

Author: Vaibhavdixit02

Diff for: benchmarks/OptimizationCUTEst/CUTEst_bounded.jmd

@@ -35,7 +35,7 @@ problems.
 
 ```julia
 optimizers = [
-        Optimization.LBFGS(),
+        #Optimization.LBFGS(),
         MOI.OptimizerWithAttributes(Ipopt.Optimizer, "print_level" => 0)
     ]
 

Diff for: benchmarks/OptimizationCUTEst/CUTEst_quadratic.jmd

@@ -35,7 +35,6 @@ problems.
 
 ```julia
 optimizers = [
-        Optimization.LBFGS(),
         MOI.OptimizerWithAttributes(Ipopt.Optimizer, "print_level" => 0)
     ]
 
@@ -90,7 +89,7 @@ Lastly, we examine the problems with a quadratic objective function and only lin
 constraints. There are 252 such problems in the suite.
 
 ```julia
-quad_problems = CUTEst.select(objtype="quadratic", contype="linear")
+quad_problems = CUTEst.select_sif_problems(objtype="quadratic", contype="linear")
 
 # Analysis
 quad_results =  run_benchmarks(quad_problems, optimizers)

Diff for: benchmarks/OptimizationCUTEst/CUTEst_unbounded.jmd

@@ -35,7 +35,6 @@ problems.
 
 ```julia
 optimizers = [
-        Optimization.LBFGS(),
         MOI.OptimizerWithAttributes(Ipopt.Optimizer, "print_level" => 0)
     ]
 
@@ -95,7 +94,7 @@ following figure shows the time to solution as a function of number of variables
 optimizer.
 
 ```julia
-eq_unb_problems = CUTEst.select(min_con=1, only_equ_con=true, only_free_var=true)
+eq_unb_problems = CUTEst.select_sif_problems(min_con=1, only_equ_con=true, only_free_var=true)
 
 # Analysis
 eq_unb_results =  run_benchmarks(eq_unb_problems, optimizers)