AdaptiveRegularization.jl/src/utils/increase_decrease.jl at c794a5afed4ba4d7fdf5cd03bfd9a5e770173d93 · JuliaSmoothOptimizers/AdaptiveRegularization.jl · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
"""
    decrease(X::TPData, α::T, TR::ARTrustRegion)

Return a decreased `α`.
"""
function decrease(X::TPData, α::T, TR::ARTrustRegion) where {T}
  return α * TR.decrease_factor
end

"""
    increase(X::TPData, α::T, TR::ARTrustRegion)

Return an increased `α`.
"""
function increase(::TPData, α::T, TR::ARTrustRegion) where {T}
  return min(α * TR.increase_factor, TR.max_α)
end

# X.indmin is between 1 and nshifts
function decrease(X::PDataKARC, α::T, TR::ARTrustRegion) where {T}
  X.indmin += 1 # the step wasn't successful so we need to change something
  α2 = max(X.norm_dirs[X.indmin] / X.shifts[X.indmin], eps(T))

  targetα = α * TR.decrease_factor

  # fix α to its "ideal" value to satisfy αλ=||d||
  # while ensuring α decreases enough
  last = findlast(X.positives)
  while !isnothing(last) && α2 > targetα && X.indmin < last
    X.indmin += 1
    α2 = max(X.norm_dirs[X.indmin] / X.shifts[X.indmin], eps(T))
  end

  if (isnothing(X.indmin) | isnothing(last) | X.indmin == last) & (α2 > targetα)
    @warn "PreProcessKARC failure: α2=$α2"
  end

  X.d .= X.xShift[X.indmin]
  X.λ = X.shifts[X.indmin]

  return α2
end

function decrease(X::PDataTRK, α::T, TR::ARTrustRegion) where {T}
  X.indmin += 1
  α2 = X.norm_dirs[X.indmin]

  # fix α to its "ideal" value to satisfy α=||d||
  # while ensuring α decreases enough
  targetα = α * TR.decrease_factor

  last = findlast(X.positives)
  while !isnothing(last) && α2 > targetα && X.indmin < last
    X.indmin += 1
    α2 = X.norm_dirs[X.indmin]
  end

  if X.indmin == last
    @warn "PreProcessTRK failure: α2=$α2"
  end

  X.d .= X.xShift[X.indmin]
  X.λ = X.shifts[X.indmin]

  return α2
end