我想用Java解决这个整数线性代数问题,其中xi,yi,zi是整数变量(所有正数,xi≥0,yi≥0,zi≥0),
而a,b,c,d,e,f,g,h是常数(正整数≥0,例如a = 20,b = 12,c = 28,d = 24,e = 19,f = 5 ,g = 6,h = 6)。
x1+x2+x3+x4+x5 = a
y1+y2+y3+y4+y5 = b
z1+z2+z3+z4+z5 = c
x1+y1+z1 = d
x2+y2+z2 = e
x3+y3+z3 = f
x4+y4+z4 = g
x5+y5+z5 = h
It could be viewed as:
- sum constraint on the rows
- sum constraint on the columns
| x1 | x2 | x3 | x4 | x5 | → sum equal to a
| y1 | y2 | y3 | y4 | y5 | → sum equal to b
| z1 | z2 | z3 | z4 | z5 | → sum equal to c
↓ ↓ ↓ ↓ ↓
d e f g h
显然,有大量的整数解决方案来解决这个问题(但不是无限的)。如果可能的话,我想轻松地收集一堆这些整数解,然后从集合中随机弹出一个整数解。
提前致谢!
解决(找到一个解决方案):
多亏了apete!我使用ojalgo找到了解决此问题的方法。
这是我的代码:
import org.ojalgo.OjAlgoUtils;
import org.ojalgo.netio.BasicLogger;
import org.ojalgo.optimisation.Expression;
import org.ojalgo.optimisation.ExpressionsBasedModel;
import org.ojalgo.optimisation.Optimisation;
import org.ojalgo.optimisation.Variable;
/**
* @author madx
*/
public abstract class ojAlgoTest {
static int[] row_constraints = new int[]{20,12,28};
static int[] col_constraints = new int[]{24,19,5,6,6};
public static void main(final String[] args) {
BasicLogger.debug();
BasicLogger.debug(ojAlgoTest.class.getSimpleName());
BasicLogger.debug(OjAlgoUtils.getTitle());
BasicLogger.debug(OjAlgoUtils.getDate());
BasicLogger.debug();
int rows = row_constraints.length;
int cols = col_constraints.length;
// Create variables expressing servings of each of the considered variable
// Set lower and upper limits on the number of servings as well as the weight (cost of a
// serving) for each variable.
final Variable matrix[][] = new Variable[rows][cols];
for(int i=0; i<rows;i++){
for(int j=0;j<cols;j++){
matrix[i][j] = Variable.make("Matrix" + i + "_" + j).lower(0).upper(24).weight(1);
}
}
// Create a model and add the variables to it.
final ExpressionsBasedModel tmpModel = new ExpressionsBasedModel();
for(int i=0; i<rows;i++){
for(int j=0;j<cols;j++){
tmpModel.addVariable(matrix[i][j]);
}
}
// Create contraints
for(int i=0; i<cols;i++){
final Expression cat = tmpModel.addExpression("Col_Constraint_"+i).lower(col_constraints[i]).upper(col_constraints[i]);
for(int j=0; j<rows;j++){
cat.setLinearFactor(matrix[j][i], 1);
}
}
for(int j=0; j<rows;j++){
final Expression cat = tmpModel.addExpression("Row_Constraint_"+j).lower(row_constraints[j]).upper(row_constraints[j]);
for(int i=0; i<cols;i++){
cat.setLinearFactor(matrix[j][i], 1);
}
}
// Solve the problem - minimise the cost
Optimisation.Result tmpResult = tmpModel.minimise();
// Print the result
BasicLogger.debug();
BasicLogger.debug(tmpResult);
BasicLogger.debug();
// Modify the model to require an integer valued solution.
BasicLogger.debug("Adding integer constraints...");
for(int i=0; i<rows;i++){
for(int j=0;j<cols;j++){
matrix[i][j].integer(true);
}
}
// Solve again
tmpResult = tmpModel.minimise();
// Print the result, and the model
BasicLogger.debug();
BasicLogger.debug(tmpResult);
BasicLogger.debug();
BasicLogger.debug(tmpModel);
BasicLogger.debug();
}
}
最佳答案
不能将其表述为网络流量问题吗? (从列约束到行约束的流,带有额外的行/列以处理差异)。如果为true,则可以使用网络单纯形算法生成整数解(所有问题参数必须为整数)。
如果我错了,那么绝对可以将其表达为一般的MIP问题。有许多免费的商业软件包可以帮助解决这些问题。 ojAlgo是一种开源的纯Java替代品(我写过)。
Here's an example关于如何使用ojAlgo建模优化问题。