Try our new documentation site (beta).


dense_vb.vb


' Copyright 2022, Gurobi Optimization, LLC
'
' This example formulates and solves the following simple QP model:
'
'   minimize    x + y + x^2 + x*y + y^2 + y*z + z^2
'   subject to  x + 2 y + 3 z >= 4
'               x +   y       >= 1
'               x, y, z non-negative
'
' The example illustrates the use of dense matrices to store A and Q
' (and dense vectors for the other relevant data).  We don't recommend
' that you use dense matrices, but this example may be helpful if you
' already have your data in this format.

Imports Gurobi

Class dense_vb

    Protected Shared Function _
      dense_optimize(env As GRBEnv, _
                     rows As Integer, _
                     cols As Integer, _
                     c As Double(), _
                     Q As Double(,), _
                     A As Double(,), _
                     sense As Char(), _
                     rhs As Double(), _
                     lb As Double(), _
                     ub As Double(), _
                     vtype As Char(), _
                     solution As Double()) As Boolean

        Dim success As Boolean = False

        Try
            Dim model As New GRBModel(env)

            ' Add variables to the model

            Dim vars As GRBVar() = model.AddVars(lb, ub, Nothing, vtype, Nothing)

            ' Populate A matrix

            For i As Integer = 0 To rows - 1
                Dim expr As New GRBLinExpr()
                For j As Integer = 0 To cols - 1
                    If A(i, j) <> 0 Then
                        expr.AddTerm(A(i, j), vars(j))
                    End If
                Next
                model.AddConstr(expr, sense(i), rhs(i), "")
            Next

            ' Populate objective

            Dim obj As New GRBQuadExpr()
            If Q IsNot Nothing Then
                For i As Integer = 0 To cols - 1
                    For j As Integer = 0 To cols - 1
                        If Q(i, j) <> 0 Then
                            obj.AddTerm(Q(i, j), vars(i), vars(j))
                        End If
                    Next
                Next
                For j As Integer = 0 To cols - 1
                    If c(j) <> 0 Then
                        obj.AddTerm(c(j), vars(j))
                    End If
                Next
                model.SetObjective(obj)
            End If

            ' Solve model

            model.Optimize()

            ' Extract solution

            If model.Status = GRB.Status.OPTIMAL Then
                success = True

                For j As Integer = 0 To cols - 1
                    solution(j) = vars(j).X
                Next
            End If

            model.Dispose()

        Catch e As GRBException
            Console.WriteLine("Error code: " & e.ErrorCode & ". " & e.Message)
        End Try

        Return success
    End Function

    Public Shared Sub Main(args As String())
        Try
            Dim env As New GRBEnv()

            Dim c As Double() = New Double() {1, 1, 0}
            Dim Q As Double(,) = New Double(,) {{1, 1, 0}, {0, 1, 1}, {0, 0, 1}}
            Dim A As Double(,) = New Double(,) {{1, 2, 3}, {1, 1, 0}}
            Dim sense As Char() = New Char() {">"C, ">"C}
            Dim rhs As Double() = New Double() {4, 1}
            Dim lb As Double() = New Double() {0, 0, 0}
            Dim success As Boolean
            Dim sol As Double() = New Double(2) {}

            success = dense_optimize(env, 2, 3, c, Q, A, sense, rhs, lb, Nothing, _
                                     Nothing, sol)

            If success Then
                Console.WriteLine("x: " & sol(0) & ", y: " & sol(1) & ", z: " & sol(2))
            End If

            ' Dispose of environment

            env.Dispose()

        Catch e As GRBException
            Console.WriteLine("Error code: " & e.ErrorCode & ". " & e.Message)
        End Try

    End Sub
End Class

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Search

Gurobi Optimization