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Fix backtracking #26

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merged 2 commits into from
Oct 4, 2023
Merged

Fix backtracking #26

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a37e80d
save on gradient evals
Oct 4, 2023
79ec1a1
return g_z instead
Oct 4, 2023
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26 changes: 13 additions & 13 deletions src/AdaProx.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -23,33 +23,33 @@ upper_bound(x, f_x, grad_x, z, gamma) = f_x + real(dot(grad_x, z - x)) + 1 / (2
# https://my.siam.org/Store/Product/viewproduct/?ProductId=29044686

function backtrack_stepsize(gamma, f, g, x, f_x, grad_x)
z, _ = prox(g, x - gamma * grad_x, gamma)
z, g_z = prox(g, x - gamma * grad_x, gamma)
ub_z = upper_bound(x, f_x, grad_x, z, gamma)
f_z = f(z)
while f_z > ub_z
gamma /= 2
if gamma < 1e-12
@error "step size became too small ($gamma)"
end
z, _ = prox(g, x - gamma * grad_x, gamma)
z, g_z = prox(g, x - gamma * grad_x, gamma)
ub_z = upper_bound(x, f_x, grad_x, z, gamma)
f_z = f(z)
end
grad_z, ~ = gradient(f, z)
return gamma, z, f_z, grad_z
return gamma, z, f_z, g_z
end

function backtracking_proxgrad(x0; f, g, gamma0, xi = 1.0 ,tol = 1e-5, maxit = 100_000, name = "Backtracking PG")
x, z, gamma = x0, x0, gamma0
grad_x, f_x = gradient(f, x)
for it = 1:maxit
gamma, z, f_z, grad_z = backtrack_stepsize(xi * gamma, f, g, x, f_x, grad_x)
gamma, z, f_z, g_z = backtrack_stepsize(xi * gamma, f, g, x, f_x, grad_x)
norm_res = norm(z - x) / gamma
@logmsg Record "" method=name it gamma norm_res objective=(nocount(f)(x) + nocount(g)(x)) grad_f_evals=grad_count(f) prox_g_evals=prox_count(g) f_evals=eval_count(f)
@logmsg Record "" method=name it gamma norm_res objective=(f_z + g_z) grad_f_evals=grad_count(f) prox_g_evals=prox_count(g) f_evals=eval_count(f)
if norm_res <= tol
return z, it
end
x, f_x, grad_x = z, f_z, grad_z
x, f_x = z, f_z
grad_x, _ = gradient(f, x)
end
return z, maxit
end
Expand All @@ -60,9 +60,9 @@ function backtracking_nesterov(x0; f, g, gamma0, tol = 1e-5, maxit = 100_000, na
grad_x, f_x = gradient(f, x)
for it = 1:maxit
z_prev = z
gamma, z, _, _ = backtrack_stepsize(gamma, f, g, x, f_x, grad_x)
gamma, z, f_z, g_z = backtrack_stepsize(gamma, f, g, x, f_x, grad_x)
norm_res = norm(z - x) / gamma
@logmsg Record "" method=name it gamma norm_res objective=(nocount(f)(x) + nocount(g)(x)) grad_f_evals=grad_count(f) prox_g_evals=prox_count(g) f_evals=eval_count(f)
@logmsg Record "" method=name it gamma norm_res objective=(f_z + g_z) grad_f_evals=grad_count(f) prox_g_evals=prox_count(g) f_evals=eval_count(f)
if norm_res <= tol
return z, it
end
Expand Down Expand Up @@ -120,9 +120,9 @@ function fixed_nesterov(
z = x + beta * (x - x_prev)
grad_z, _ = gradient(f, z)
x_prev = x
x, _ = prox(g, z - gamma * grad_z, gamma)
x, g_x = prox(g, z - gamma * grad_z, gamma)
norm_res = norm(x - z) / gamma
@logmsg Record "" method=name it gamma norm_res objective=(nocount(f)(x) + nocount(g)(x)) grad_f_evals=grad_count(f) prox_g_evals=prox_count(g) f_evals=eval_count(f)
@logmsg Record "" method=name it gamma norm_res objective=(nocount(f)(x) + g_x) grad_f_evals=grad_count(f) prox_g_evals=prox_count(g) f_evals=eval_count(f)
if norm_res <= tol
return x, it
end
Expand Down Expand Up @@ -167,9 +167,9 @@ function agraal(
theta = phi * gamma / gamma_prev
x_bar = ((phi - 1) * x + x_bar) / phi
x_prev, grad_x_prev = x, grad_x
x, _ = prox(g, x_bar - gamma * grad_x_prev, gamma)
x, g_x = prox(g, x_bar - gamma * grad_x_prev, gamma)
norm_res = norm(x - x_prev) / gamma
@logmsg Record "" method=name it gamma norm_res objective=(nocount(f)(x) + nocount(g)(x)) grad_f_evals=grad_count(f) prox_g_evals=prox_count(g) f_evals=eval_count(f)
@logmsg Record "" method=name it gamma norm_res objective=(nocount(f)(x) + g_x) grad_f_evals=grad_count(f) prox_g_evals=prox_count(g) f_evals=eval_count(f)
if norm_res <= tol
return x, it
end
Expand Down
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