本文整理汇总了Golang中github.com/gonum/matrix/mat64.Vector.SolveVec方法的典型用法代码示例。如果您正苦于以下问题：Golang Vector.SolveVec方法的具体用法？Golang Vector.SolveVec怎么用？Golang Vector.SolveVec使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类github.com/gonum/matrix/mat64.Vector的用法示例。
在下文中一共展示了Vector.SolveVec方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Golang代码示例。
示例1: simplex
//.........这里部分代码省略.........
r := make([]float64, n-m)
move := make([]float64, m)
// Solve the linear program starting from the initial feasible set. This is
// the "Phase 2" problem.
//
// Algorithm:
// 1) Compute the "reduced costs" for the non-basic variables. The reduced
// costs are the lagrange multipliers of the constraints.
// r = cn - an^T * ab^-T * cb
// 2) If all of the reduced costs are positive, no improvement is possible,
// and the solution is optimal (xn can only increase because of
// non-negativity constraints). Otherwise, the solution can be improved and
// one element will be exchanged in the basic set.
// 3) Choose the x_n with the most negative value of r. Call this value xe.
// This variable will be swapped into the basic set.
// 4) Increase xe until the next constraint boundary is met. This will happen
// when the first element in xb becomes 0. The distance xe can increase before
// a given element in xb becomes negative can be found from
// xb = Ab^-1 b - Ab^-1 An xn
// = Ab^-1 b - Ab^-1 Ae xe
// = bhat + d x_e
// xe = bhat_i / - d_i
// where Ae is the column of A corresponding to xe.
// The constraining basic index is the first index for which this is true,
// so remove the element which is min_i (bhat_i / -d_i), assuming d_i is negative.
// If no d_i is less than 0, then the problem is unbounded.
// 5) If the new xe is 0 (that is, bhat_i == 0), then this location is at
// the intersection of several constraints. Use the Bland rule instead
// of the rule in step 4 to avoid cycling.
for {
// Compute reduced costs -- r = cn - an^T ab^-T cb
var tmp mat64.Vector
err = tmp.SolveVec(ab.T(), cbVec)
if err != nil {
break
}
data := make([]float64, n-m)
tmp2 := mat64.NewVector(n-m, data)
tmp2.MulVec(an.T(), &tmp)
floats.SubTo(r, cn, data)
// Replace the most negative element in the simplex. If there are no
// negative entries then the optimal solution has been found.
minIdx := floats.MinIdx(r)
if r[minIdx] >= -tol {
break
}
for i, v := range r {
if math.Abs(v) < rRoundTol {
r[i] = 0
}
}
// Compute the moving distance.
err = computeMove(move, minIdx, A, ab, xb, nonBasicIdx)
if err != nil {
if err == ErrUnbounded {
return math.Inf(-1), nil, nil, ErrUnbounded
}
break
}
// Replace the basic index along the tightest constraint.
replace := floats.MinIdx(move)