func (u *uniGradStruct) Initialize() error {
initLoc := u.loc.Init()
initObj := u.obj.Init()
initGrad := u.grad.Init()
// The initial values need to both be NaN or both not nan
if math.IsNaN(initObj) {
if !math.IsNaN(initGrad) {
return errors.New("gofunopter: cubic: initial function value and gradient must either both be set or neither set")
}
// Both nan, so compute the initial fuction value and gradient
initObj, initGrad, err := u.fun.ObjGrad(initLoc)
if err != nil {
return errors.New("gofunopter: cubic: error calling function during optimization")
}
u.obj.SetInit(initObj)
u.grad.SetInit(initGrad)
} else {
if math.IsNaN(initGrad) {
return errors.New("gofunopter: cubic: initial function value and gradient must either both be set or neither set")
}
}
err := optimize.Initialize(u.loc, u.obj, u.grad)
if err != nil {
return err
}
err = u.optimizer.Initialize(u.loc, u.obj, u.grad)
if err != nil {
return err
}
return nil
}
func (lbfgs *Lbfgs) Initialize(loc *multi.Location, obj *uni.Objective, grad *multi.Gradient) error {
lbfgs.nDim = len(loc.Init())
// Now initialize the three to set the initial location to the current location
err := optimize.Initialize(lbfgs.step)
if err != nil {
return errors.New("lbfgs: error initializing: " + err.Error())
}
// Initialize rest of memory
// Replace this with overwriting?
lbfgs.q = make([]float64, lbfgs.nDim)
lbfgs.a = make([]float64, lbfgs.NumStore)
lbfgs.b = make([]float64, lbfgs.NumStore)
lbfgs.sHist = make([][]float64, lbfgs.NumStore)
lbfgs.yHist = make([][]float64, lbfgs.NumStore)
lbfgs.rhoHist = make([]float64, lbfgs.NumStore)
for i := range lbfgs.sHist {
lbfgs.sHist[i] = make([]float64, lbfgs.nDim)
lbfgs.yHist[i] = make([]float64, lbfgs.nDim)
}
lbfgs.gamma_k = 1.0
lbfgs.tmp = make([]float64, lbfgs.nDim)
lbfgs.p_k = make([]float64, lbfgs.nDim)
lbfgs.s_k = make([]float64, lbfgs.nDim)
lbfgs.y_k = make([]float64, lbfgs.nDim)
lbfgs.z = make([]float64, lbfgs.nDim)
return nil
}
func (m *multiGradStruct) Initialize() error {
initLoc := m.loc.Init()
initObj := m.obj.Init()
initGrad := m.grad.Init()
// The initial values need to both be NaN or both not nan
if math.IsNaN(initObj) {
if len(initGrad) != 0 {
return errors.New("initial function value and gradient must either both be set or neither set")
}
// Both nan, so compute the initial fuction value and gradient
initObj, initGrad, err := m.fun.ObjGrad(initLoc)
if err != nil {
return errors.New("error calling function during optimization: \n" + err.Error())
}
m.obj.SetInit(initObj)
m.grad.SetInit(initGrad)
} else {
if len(initGrad) == 0 {
return errors.New("initial function value and gradient must either both be set or neither set")
}
}
err := optimize.Initialize(m.loc, m.obj, m.grad)
if err != nil {
return err
}
err = m.optimizer.Initialize(m.loc, m.obj, m.grad)
if err != nil {
return err
}
return nil
}