func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol float64) (float64, []float64, []int, error) {
err := verifyInputs(initialBasic, c, A, b)
if err != nil {
if err == ErrUnbounded {
return math.Inf(-1), nil, nil, ErrUnbounded
}
return math.NaN(), nil, nil, err
}
m, n := A.Dims()
// There is at least one optimal solution to the LP which is at the intersection
// to a set of constraint boundaries. For a standard form LP with m variables
// and n equality constraints, at least m-n elements of x must equal zero
// at optimality. The Simplex algorithm solves the standard-form LP by starting
// at an initial constraint vertex and successively moving to adjacent constraint
// vertices. At every vertex, the set of non-zero x values is the "basic
// feasible solution". The list of non-zero x's are maintained in basicIdxs,
// the respective columns of A are in ab, and the actual non-zero values of
// x are in xb.
//
// The LP is equality constrained such that A * x = b. This can be expanded
// to
// ab * xb + an * xn = b
// where ab are the columns of a in the basic set, and an are all of the
// other columns. Since each element of xn is zero by definition, this means
// that for all feasible solutions xb = ab^-1 * b.
//
// Before the simplex algorithm can start, an initial feasible solution must
// be found. If initialBasic is non-nil a feasible solution has been supplied.
// Otherwise the "Phase I" problem must be solved to find an initial feasible
// solution.
var basicIdxs []int // The indices of the non-zero x values.
var ab *mat64.Dense // The subset of columns of A listed in basicIdxs.
var xb []float64 // The non-zero elements of x. xb = ab^-1 b
if initialBasic != nil {
// InitialBasic supplied. Panic if incorrect length or infeasible.
if len(initialBasic) != m {
panic("lp: incorrect number of initial vectors")
}
ab = extractColumns(A, initialBasic)
xb, err = initializeFromBasic(ab, b)
if err != nil {
panic(err)
}
basicIdxs = make([]int, len(initialBasic))
copy(basicIdxs, initialBasic)
} else {
// No inital basis supplied. Solve the PhaseI problem.
basicIdxs, ab, xb, err = findInitialBasic(A, b)
if err != nil {
return math.NaN(), nil, nil, err
}
}
// basicIdxs contains the indexes for an initial feasible solution,
// ab contains the extracted columns of A, and xb contains the feasible
// solution. All x not in the basic set are 0 by construction.
// nonBasicIdx is the set of nonbasic variables.
nonBasicIdx := make([]int, 0, n-m)
inBasic := make(map[int]struct{})
for _, v := range basicIdxs {
inBasic[v] = struct{}{}
}
for i := 0; i < n; i++ {
_, ok := inBasic[i]
if !ok {
nonBasicIdx = append(nonBasicIdx, i)
}
}
// cb is the subset of c for the basic variables. an and cn
// are the equivalents to ab and cb but for the nonbasic variables.
cb := make([]float64, len(basicIdxs))
for i, idx := range basicIdxs {
cb[i] = c[idx]
}
cn := make([]float64, len(nonBasicIdx))
for i, idx := range nonBasicIdx {
cn[i] = c[idx]
}
an := extractColumns(A, nonBasicIdx)
bVec := mat64.NewVector(len(b), b)
cbVec := mat64.NewVector(len(cb), cb)
// Temporary data needed each iteration. (Described later)
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)
if move[replace] <= 0 {
replace, minIdx, err = replaceBland(A, ab, xb, basicIdxs, nonBasicIdx, r, move)
if err != nil {
if err == ErrUnbounded {
return math.Inf(-1), nil, nil, ErrUnbounded
}
break
}
}
// Replace the constrained basicIdx with the newIdx.
basicIdxs[replace], nonBasicIdx[minIdx] = nonBasicIdx[minIdx], basicIdxs[replace]
cb[replace], cn[minIdx] = cn[minIdx], cb[replace]
tmpCol1 := mat64.Col(nil, replace, ab)
tmpCol2 := mat64.Col(nil, minIdx, an)
ab.SetCol(replace, tmpCol2)
an.SetCol(minIdx, tmpCol1)
// Compute the new xb.
xbVec := mat64.NewVector(len(xb), xb)
err = xbVec.SolveVec(ab, bVec)
if err != nil {
break
}
}
// Found the optimum successfully or died trying. The basic variables get
// their values, and the non-basic variables are all zero.
opt := floats.Dot(cb, xb)
xopt := make([]float64, n)
for i, v := range basicIdxs {
xopt[v] = xb[i]
}
return opt, xopt, basicIdxs, err
}