Apache common SimplexSolver ObjectiveFunction 用于最大化矩阵中值的总和
Apache common SimplexSolver ObjectiveFunction for maximizing the sum of values in a matrix
我正在尝试使用 apache-commons 中的单纯形求解器来解决以下线性问题:org.apache.commons.math3.optim.linear.SimplexSolver
。
n
是行数
m
是列数
L
是每行总和值的全局限制
这是我目前拥有的:
List<LinearConstraint> constraints = new ArrayList<>();
double[][] A = calculateAValues();
// m = count of columns
// constraint 1: the sum of values in all column must be <= 1
for(int i = 0; i < m; i++) {
double[] v = new double[n];
for(int j=0; j < n; j++) {
v[j] = 1;
}
constraints.add(new LinearConstraint(v, Relationship.LEQ, 1));
}
// n = count of rows
// constraint 2: sum of a_i,j in all row must be <= L (Limit)
for(int i = 0; i < n; i++) {
double[] v = new double[m];
for(int j=0; j < m; j++) {
v[j] = A[i][j];
}
constraints.add(new LinearConstraint(v, Relationship.LEQ, L));
}
double[] objectiveCoefficients = new double[n * m];
for(int i = 0; i < n * m; ++i) {
objectiveCoefficients[i] = 1;
}
LinearObjectiveFunction objective = new LinearObjectiveFunction(objectiveCoefficients, 0);
LinearConstraintSet constraintSet = new LinearConstraintSet(constraints);
SimplexSolver solver = new SimplexSolver();
PointValuePair solution = solver.optimize(objective, constraintSet, GoalType.MAXIMIZE);
return solution.getValue();
我无法正确使用 objective 函数,而且可能还缺少其他一些东西。到目前为止,我的每一次尝试都得到了 UnboundedSolutionException
.
错误似乎在线性约束的系数数组中。
您有 n*m
个变量,因此约束的系数数组和 objective 函数的长度必须为 n*m
。不幸的是,如果 SimplexSolver
比 objective 函数的数组短,则 SimplexSolver
会默默地扩展约束数组。所以你的代码没有指定导致无限解决方案的正确约束。
约束 1:所有列中值的总和必须 <= 1
for(int j=0; j<m; j++)
{
double[] v = new double[n*m];
for(int i=0; i<n; i++)
v[i*n + j] = 1;
constraints.add(new LinearConstraint(v, Relationship.LEQ, 1));
}
约束2:所有行中a_i,j的和必须<= L (Limit)
// n = count of rows
for(int i=0; i<n; i++)
{
double[] v = new double[n*m];
for(int j=0; j<m; j++)
v[i*n + j] = A[i][j];
constraints.add(new LinearConstraint(v, Relationship.LEQ, L));
}
Objective咖啡机:
double[] objectiveCoefficients = new double[n * m];
Arrays.fill(objectiveCoefficients, 1.0);
LinearObjectiveFunction objective = LinearObjectiveFunction(objectiveCoefficients, 0);
由于约束 2,约束 x_ij <= 1
已经满足。
也许使用 NonNegativeConstraint
:
为 0 <= x_ij
显式指定约束会让事情变得更清楚
SimplexSolver solver = new SimplexSolver();
PointValuePair solution = solver.optimize(objective, constraintSet,
GoalType.MAXIMIZE, new NonNegativeConstraint(true));
我正在尝试使用 apache-commons 中的单纯形求解器来解决以下线性问题:org.apache.commons.math3.optim.linear.SimplexSolver
。
n
是行数
m
是列数
L
是每行总和值的全局限制
这是我目前拥有的:
List<LinearConstraint> constraints = new ArrayList<>();
double[][] A = calculateAValues();
// m = count of columns
// constraint 1: the sum of values in all column must be <= 1
for(int i = 0; i < m; i++) {
double[] v = new double[n];
for(int j=0; j < n; j++) {
v[j] = 1;
}
constraints.add(new LinearConstraint(v, Relationship.LEQ, 1));
}
// n = count of rows
// constraint 2: sum of a_i,j in all row must be <= L (Limit)
for(int i = 0; i < n; i++) {
double[] v = new double[m];
for(int j=0; j < m; j++) {
v[j] = A[i][j];
}
constraints.add(new LinearConstraint(v, Relationship.LEQ, L));
}
double[] objectiveCoefficients = new double[n * m];
for(int i = 0; i < n * m; ++i) {
objectiveCoefficients[i] = 1;
}
LinearObjectiveFunction objective = new LinearObjectiveFunction(objectiveCoefficients, 0);
LinearConstraintSet constraintSet = new LinearConstraintSet(constraints);
SimplexSolver solver = new SimplexSolver();
PointValuePair solution = solver.optimize(objective, constraintSet, GoalType.MAXIMIZE);
return solution.getValue();
我无法正确使用 objective 函数,而且可能还缺少其他一些东西。到目前为止,我的每一次尝试都得到了 UnboundedSolutionException
.
错误似乎在线性约束的系数数组中。
您有 n*m
个变量,因此约束的系数数组和 objective 函数的长度必须为 n*m
。不幸的是,如果 SimplexSolver
比 objective 函数的数组短,则 SimplexSolver
会默默地扩展约束数组。所以你的代码没有指定导致无限解决方案的正确约束。
约束 1:所有列中值的总和必须 <= 1
for(int j=0; j<m; j++)
{
double[] v = new double[n*m];
for(int i=0; i<n; i++)
v[i*n + j] = 1;
constraints.add(new LinearConstraint(v, Relationship.LEQ, 1));
}
约束2:所有行中a_i,j的和必须<= L (Limit)
// n = count of rows
for(int i=0; i<n; i++)
{
double[] v = new double[n*m];
for(int j=0; j<m; j++)
v[i*n + j] = A[i][j];
constraints.add(new LinearConstraint(v, Relationship.LEQ, L));
}
Objective咖啡机:
double[] objectiveCoefficients = new double[n * m];
Arrays.fill(objectiveCoefficients, 1.0);
LinearObjectiveFunction objective = LinearObjectiveFunction(objectiveCoefficients, 0);
由于约束 2,约束 x_ij <= 1
已经满足。
也许使用 NonNegativeConstraint
:
0 <= x_ij
显式指定约束会让事情变得更清楚
SimplexSolver solver = new SimplexSolver();
PointValuePair solution = solver.optimize(objective, constraintSet,
GoalType.MAXIMIZE, new NonNegativeConstraint(true));