func TestLinprog(t *testing.T) {
m := 500
n := 1000
tol := 1e-8
A := mat.RandN(m, n)
c := mat.RandVec(n)
b := mat.NewVec(m)
xt := mat.RandVec(n)
b.Apply(A, xt)
At := A.TrView()
rd := mat.NewVec(n)
rp := mat.NewVec(m)
rs := mat.NewVec(n)
prob := NewStandard(c, A, b)
//Example for printing duality gap and infeasibilities
result := Solve(prob, nil, NewDisplay(2))
rd.Sub(c, result.S)
rd.AddMul(At, result.Y, -1)
rp.Apply(A, result.X)
rp.Sub(b, rp)
rs.Mul(result.X, result.S)
rs.Neg(rs)
dev := (rd.Asum() + rp.Asum() + rs.Asum()) / float64(n)
if dev > tol {
t.Log(dev)
t.Fail()
}
}
func BenchmarkLinprog(bench *testing.B) {
bench.StopTimer()
m := 50
n := 100
tol := 1e-3
rd := mat.NewVec(n)
rp := mat.NewVec(m)
rs := mat.NewVec(n)
for i := 0; i < bench.N; i++ {
A := mat.RandN(m, n)
c := mat.RandVec(n)
b := mat.NewVec(m)
xt := mat.RandVec(n)
b.Apply(A, xt)
At := A.TrView()
prob := NewStandard(c, A, b)
bench.StartTimer()
result := Solve(prob, nil)
bench.StopTimer()
rd.Sub(c, result.S)
rd.AddMul(At, result.Y, -1)
rp.Apply(A, result.X)
rp.Sub(b, rp)
rs.Mul(result.X, result.S)
rs.Neg(rs)
dev := (rd.Asum() + rp.Asum() + rs.Asum()) / float64(n)
if dev > tol {
bench.Log(dev)
}
}
}
func TestQuadratic(t *testing.T) {
mat.Register(cops)
n := 10
xStar := mat.NewVec(n)
xStar.AddSc(1)
A := mat.RandN(n)
At := A.TrView()
AtA := mat.New(n)
AtA.Mul(At, A)
bTmp := mat.NewVec(n)
bTmp.Apply(A, xStar)
b := mat.NewVec(n)
b.Apply(At, bTmp)
b.Scal(-2)
c := bTmp.Nrm2Sq()
//Define input arguments
obj := opt.NewQuadratic(AtA, b, c)
p := NewParams()
sol := NewSolution(mat.NewVec(n))
//Steepest descent with armijo
stDesc := NewSteepestDescent()
res1 := stDesc.Solve(obj, sol, p, NewDisplay(100))
t.Log(res1.ObjX, res1.FunEvals, res1.GradEvals, res1.Status)
//Steepest descent with Quadratic
stDesc.LineSearch = uni.DerivWrapper{uni.NewQuadratic()}
res2 := stDesc.Solve(obj, sol, p, NewDisplay(100))
t.Log(res2.ObjX, res2.FunEvals, res2.GradEvals, res2.Status)
//LBFGS with armijo
lbfgs := NewLBFGS()
res3 := lbfgs.Solve(obj, sol, p, NewDisplay(10))
t.Log(res3.ObjX, res3.FunEvals, res3.GradEvals, res3.Status)
//constrained problems (constraints described as projection)
projGrad := NewProjGrad()
res4 := projGrad.Solve(obj, opt.RealPlus{}, sol, p, NewDisplay(100))
t.Log(res4.ObjX, res4.FunEvals, res4.GradEvals, res4.Status)
if math.Abs(res1.ObjX) > 0.01 {
t.Fail()
}
if math.Abs(res2.ObjX) > 0.01 {
t.Fail()
}
if math.Abs(res3.ObjX) > 0.01 {
t.Fail()
}
if math.Abs(res4.ObjX) > 0.01 {
t.Fail()
}
}