Dense Examples#
This section includes source code for all of the Gurobi dense examples.
The same source code can be found in the examples
directory of the
Gurobi distribution.
/* Copyright 2025, 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.
*/
#include <stdlib.h>
#include <stdio.h>
#include "gurobi_c.h"
/*
Solve an LP/QP/MILP/MIQP represented using dense matrices. This
routine assumes that A and Q are both stored in row-major order.
It returns 1 if the optimization succeeds. When successful,
it returns the optimal objective value in 'objvalP', and the
optimal solution vector in 'solution'.
*/
static int
dense_optimize(GRBenv *env,
int rows,
int cols,
double *c, /* linear portion of objective function */
double *Q, /* quadratic portion of objective function */
double *A, /* constraint matrix */
char *sense, /* constraint senses */
double *rhs, /* RHS vector */
double *lb, /* variable lower bounds */
double *ub, /* variable upper bounds */
char *vtype, /* variable types (continuous, binary, etc.) */
double *solution,
double *objvalP)
{
GRBmodel *model = NULL;
int i, j, optimstatus;
int error = 0;
int success = 0;
/* Create an empty model */
error = GRBnewmodel(env, &model, "dense", cols, c, lb, ub, vtype, NULL);
if (error) goto QUIT;
error = GRBaddconstrs(model, rows, 0, NULL, NULL, NULL, sense, rhs, NULL);
if (error) goto QUIT;
/* Populate A matrix */
for (i = 0; i < rows; i++) {
for (j = 0; j < cols; j++) {
if (A[i*cols+j] != 0) {
error = GRBchgcoeffs(model, 1, &i, &j, &A[i*cols+j]);
if (error) goto QUIT;
}
}
}
/* Populate Q matrix */
if (Q) {
for (i = 0; i < cols; i++) {
for (j = 0; j < cols; j++) {
if (Q[i*cols+j] != 0) {
error = GRBaddqpterms(model, 1, &i, &j, &Q[i*cols+j]);
if (error) goto QUIT;
}
}
}
}
/* Optimize model */
error = GRBoptimize(model);
if (error) goto QUIT;
/* Write model to 'dense.lp' */
error = GRBwrite(model, "dense.lp");
if (error) goto QUIT;
/* Capture solution information */
error = GRBgetintattr(model, GRB_INT_ATTR_STATUS, &optimstatus);
if (error) goto QUIT;
if (optimstatus == GRB_OPTIMAL) {
error = GRBgetdblattr(model, GRB_DBL_ATTR_OBJVAL, objvalP);
if (error) goto QUIT;
error = GRBgetdblattrarray(model, GRB_DBL_ATTR_X, 0, cols, solution);
if (error) goto QUIT;
success = 1;
}
QUIT:
/* Error reporting */
if (error) {
printf("ERROR: %s\n", GRBgeterrormsg(env));
exit(1);
}
/* Free model */
GRBfreemodel(model);
return success;
}
int
main(int argc,
char *argv[])
{
GRBenv *env = NULL;
int error = 0;
double c[] = {1, 1, 0};
double Q[3][3] = {{1, 1, 0}, {0, 1, 1}, {0, 0, 1}};
double A[2][3] = {{1, 2, 3}, {1, 1, 0}};
char sense[] = {'>', '>'};
double rhs[] = {4, 1};
double lb[] = {0, 0, 0};
double sol[3];
int solved;
double objval;
/* Create environment */
error = GRBloadenv(&env, "dense.log");
if (error) goto QUIT;
/* Solve the model */
solved = dense_optimize(env, 2, 3, c, &Q[0][0], &A[0][0], sense, rhs, lb,
NULL, NULL, sol, &objval);
if (solved)
printf("Solved: x=%.4f, y=%.4f, z=%.4f\n", sol[0], sol[1], sol[2]);
QUIT:
/* Free environment */
GRBfreeenv(env);
return 0;
}
/* Copyright 2025, 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.
*/
#include "gurobi_c++.h"
using namespace std;
static bool
dense_optimize(GRBEnv* env,
int rows,
int cols,
double* c, /* linear portion of objective function */
double* Q, /* quadratic portion of objective function */
double* A, /* constraint matrix */
char* sense, /* constraint senses */
double* rhs, /* RHS vector */
double* lb, /* variable lower bounds */
double* ub, /* variable upper bounds */
char* vtype, /* variable types (continuous, binary, etc.) */
double* solution,
double* objvalP)
{
GRBModel model = GRBModel(*env);
int i, j;
bool success = false;
/* Add variables to the model */
GRBVar* vars = model.addVars(lb, ub, NULL, vtype, NULL, cols);
/* Populate A matrix */
for (i = 0; i < rows; i++) {
GRBLinExpr lhs = 0;
for (j = 0; j < cols; j++)
if (A[i*cols+j] != 0)
lhs += A[i*cols+j]*vars[j];
model.addConstr(lhs, sense[i], rhs[i]);
}
GRBQuadExpr obj = 0;
for (j = 0; j < cols; j++)
obj += c[j]*vars[j];
for (i = 0; i < cols; i++)
for (j = 0; j < cols; j++)
if (Q[i*cols+j] != 0)
obj += Q[i*cols+j]*vars[i]*vars[j];
model.setObjective(obj);
model.optimize();
model.write("dense.lp");
if (model.get(GRB_IntAttr_Status) == GRB_OPTIMAL) {
*objvalP = model.get(GRB_DoubleAttr_ObjVal);
for (i = 0; i < cols; i++)
solution[i] = vars[i].get(GRB_DoubleAttr_X);
success = true;
}
delete[] vars;
return success;
}
int
main(int argc,
char *argv[])
{
GRBEnv* env = 0;
try {
env = new GRBEnv();
double c[] = {1, 1, 0};
double Q[3][3] = {{1, 1, 0}, {0, 1, 1}, {0, 0, 1}};
double A[2][3] = {{1, 2, 3}, {1, 1, 0}};
char sense[] = {'>', '>'};
double rhs[] = {4, 1};
double lb[] = {0, 0, 0};
bool success;
double objval, sol[3];
success = dense_optimize(env, 2, 3, c, &Q[0][0], &A[0][0], sense, rhs,
lb, NULL, NULL, sol, &objval);
cout << "optimal=" << success << " x: " << sol[0] << " y: " << sol[1] << " z: " << sol[2] << endl;
} catch(GRBException e) {
cout << "Error code = " << e.getErrorCode() << endl;
cout << e.getMessage() << endl;
} catch(...) {
cout << "Exception during optimization" << endl;
}
delete env;
return 0;
}
/* Copyright 2025, 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.
*/
using System;
using Gurobi;
class dense_cs {
protected static bool
dense_optimize(GRBEnv env,
int rows,
int cols,
double[] c, // linear portion of objective function
double[,] Q, // quadratic portion of objective function
double[,] A, // constraint matrix
char[] sense, // constraint senses
double[] rhs, // RHS vector
double[] lb, // variable lower bounds
double[] ub, // variable upper bounds
char[] vtype, // variable types (continuous, binary, etc.)
double[] solution) {
bool success = false;
try {
GRBModel model = new GRBModel(env);
// Add variables to the model
GRBVar[] vars = model.AddVars(lb, ub, null, vtype, null);
// Populate A matrix
for (int i = 0; i < rows; i++) {
GRBLinExpr expr = new GRBLinExpr();
for (int j = 0; j < cols; j++)
if (A[i,j] != 0)
expr.AddTerm(A[i,j], vars[j]); // Note: '+=' would be much slower
model.AddConstr(expr, sense[i], rhs[i], "");
}
// Populate objective
GRBQuadExpr obj = new GRBQuadExpr();
if (Q != null) {
for (int i = 0; i < cols; i++)
for (int j = 0; j < cols; j++)
if (Q[i,j] != 0)
obj.AddTerm(Q[i,j], vars[i], vars[j]); // Note: '+=' would be much slower
for (int j = 0; j < cols; j++)
if (c[j] != 0)
obj.AddTerm(c[j], vars[j]); // Note: '+=' would be much slower
model.SetObjective(obj);
}
// Solve model
model.Optimize();
// Extract solution
if (model.Status == GRB.Status.OPTIMAL) {
success = true;
for (int j = 0; j < cols; j++)
solution[j] = vars[j].X;
}
model.Dispose();
} catch (GRBException e) {
Console.WriteLine("Error code: " + e.ErrorCode + ". " + e.Message);
}
return success;
}
public static void Main(String[] args) {
try {
GRBEnv env = new GRBEnv();
double[] c = new double[] {1, 1, 0};
double[,] Q = new double[,] {{1, 1, 0}, {0, 1, 1}, {0, 0, 1}};
double[,] A = new double[,] {{1, 2, 3}, {1, 1, 0}};
char[] sense = new char[] {'>', '>'};
double[] rhs = new double[] {4, 1};
double[] lb = new double[] {0, 0, 0};
bool success;
double[] sol = new double[3];
success = dense_optimize(env, 2, 3, c, Q, A, sense, rhs,
lb, null, null, sol);
if (success) {
Console.WriteLine("x: " + sol[0] + ", y: " + sol[1] + ", z: " + sol[2]);
}
// Dispose of environment
env.Dispose();
} catch (GRBException e) {
Console.WriteLine("Error code: " + e.ErrorCode + ". " + e.Message);
}
}
}
/* Copyright 2025, 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.
*/
import com.gurobi.gurobi.*;
public class Dense {
protected static boolean
dense_optimize(GRBEnv env,
int rows,
int cols,
double[] c, // linear portion of objective function
double[][] Q, // quadratic portion of objective function
double[][] A, // constraint matrix
char[] sense, // constraint senses
double[] rhs, // RHS vector
double[] lb, // variable lower bounds
double[] ub, // variable upper bounds
char[] vtype, // variable types (continuous, binary, etc.)
double[] solution) {
boolean success = false;
try {
GRBModel model = new GRBModel(env);
// Add variables to the model
GRBVar[] vars = model.addVars(lb, ub, null, vtype, null);
// Populate A matrix
for (int i = 0; i < rows; i++) {
GRBLinExpr expr = new GRBLinExpr();
for (int j = 0; j < cols; j++)
if (A[i][j] != 0)
expr.addTerm(A[i][j], vars[j]);
model.addConstr(expr, sense[i], rhs[i], "");
}
// Populate objective
GRBQuadExpr obj = new GRBQuadExpr();
if (Q != null) {
for (int i = 0; i < cols; i++)
for (int j = 0; j < cols; j++)
if (Q[i][j] != 0)
obj.addTerm(Q[i][j], vars[i], vars[j]);
for (int j = 0; j < cols; j++)
if (c[j] != 0)
obj.addTerm(c[j], vars[j]);
model.setObjective(obj);
}
// Solve model
model.optimize();
// Extract solution
if (model.get(GRB.IntAttr.Status) == GRB.Status.OPTIMAL) {
success = true;
for (int j = 0; j < cols; j++)
solution[j] = vars[j].get(GRB.DoubleAttr.X);
}
model.dispose();
} catch (GRBException e) {
System.out.println("Error code: " + e.getErrorCode() + ". " +
e.getMessage());
e.printStackTrace();
}
return success;
}
public static void main(String[] args) {
try {
GRBEnv env = new GRBEnv();
double c[] = new double[] {1, 1, 0};
double Q[][] = new double[][] {{1, 1, 0}, {0, 1, 1}, {0, 0, 1}};
double A[][] = new double[][] {{1, 2, 3}, {1, 1, 0}};
char sense[] = new char[] {'>', '>'};
double rhs[] = new double[] {4, 1};
double lb[] = new double[] {0, 0, 0};
boolean success;
double sol[] = new double[3];
success = dense_optimize(env, 2, 3, c, Q, A, sense, rhs,
lb, null, null, sol);
if (success) {
System.out.println("x: " + sol[0] + ", y: " + sol[1] + ", z: " + sol[2]);
}
// Dispose of environment
env.dispose();
} catch (GRBException e) {
System.out.println("Error code: " + e.getErrorCode() + ". " +
e.getMessage());
e.printStackTrace();
}
}
}
#!/usr/bin/env python3.11
# Copyright 2025, 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.
import sys
import gurobipy as gp
from gurobipy import GRB
def dense_optimize(rows, cols, c, Q, A, sense, rhs, lb, ub, vtype, solution):
model = gp.Model()
# Add variables to model
vars = []
for j in range(cols):
vars.append(model.addVar(lb=lb[j], ub=ub[j], vtype=vtype[j]))
# Populate A matrix
for i in range(rows):
expr = gp.LinExpr()
for j in range(cols):
if A[i][j] != 0:
expr += A[i][j] * vars[j]
model.addLConstr(expr, sense[i], rhs[i])
# Populate objective
obj = gp.QuadExpr()
for i in range(cols):
for j in range(cols):
if Q[i][j] != 0:
obj += Q[i][j] * vars[i] * vars[j]
for j in range(cols):
if c[j] != 0:
obj += c[j] * vars[j]
model.setObjective(obj)
# Solve
model.optimize()
# Write model to a file
model.write("dense.lp")
if model.status == GRB.OPTIMAL:
x = model.getAttr("X", vars)
for i in range(cols):
solution[i] = x[i]
return True
else:
return False
# Put model data into dense matrices
c = [1, 1, 0]
Q = [[1, 1, 0], [0, 1, 1], [0, 0, 1]]
A = [[1, 2, 3], [1, 1, 0]]
sense = [GRB.GREATER_EQUAL, GRB.GREATER_EQUAL]
rhs = [4, 1]
lb = [0, 0, 0]
ub = [GRB.INFINITY, GRB.INFINITY, GRB.INFINITY]
vtype = [GRB.CONTINUOUS, GRB.CONTINUOUS, GRB.CONTINUOUS]
sol = [0] * 3
# Optimize
success = dense_optimize(2, 3, c, Q, A, sense, rhs, lb, ub, vtype, sol)
if success:
print(f"x: {sol[0]:g}, y: {sol[1]:g}, z: {sol[2]:g}")
' Copyright 2025, 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