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Using np.minimum instead of default min #162

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Oct 24, 2024
Merged

Using np.minimum instead of default min #162

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0779e82
Using np.minimum instead of default min
jmlarson1 Oct 24, 2024
3ea15a3
Might as well be safe with max
jmlarson1 Oct 24, 2024
ee67aaf
black
jmlarson1 Oct 24, 2024
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10 changes: 5 additions & 5 deletions pounders/py/pounders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
def _default_model_par_values(n):
par = np.zeros(4)
par[0] = np.sqrt(n)
par[1] = max(10, np.sqrt(n))
par[1] = np.maximum(10, np.sqrt(n))
par[2] = 10**-3
par[3] = 0.001

Expand Down Expand Up @@ -129,8 +129,8 @@ def pounders(Ffun, X_0, n, nf_max, g_tol, delta_0, m, Low, Upp, Prior=None, Opti
nfs = Prior["nfs"]
delta = delta_0
spsolver = Options.get("spsolver", 2)
delta_max = Options.get("delta_max", min(0.5 * np.min(Upp - Low), (10**3) * delta))
delta_min = Options.get("delta_min", min(delta * (10**-13), g_tol / 10))
delta_max = Options.get("delta_max", np.minimum(0.5 * np.min(Upp - Low), (10**3) * delta))
delta_min = Options.get("delta_min", np.minimum(delta * (10**-13), g_tol / 10))
gamma_dec = Options.get("gamma_dec", 0.5)
gamma_inc = Options.get("gamma_inc", 2)
eta_1 = Options.get("eta1", 0.05)
Expand Down Expand Up @@ -243,7 +243,7 @@ def pounders(Ffun, X_0, n, nf_max, g_tol, delta_0, m, Low, Upp, Prior=None, Opti
# input("Enter a key and press Enter to continue\n") - Don't uncomment when using Pytest with test_pounders.py
# 2. Critically test invoked if the projected model gradient is small
if ng < g_tol:
delta = max(g_tol, np.max(np.abs(X[xk_in])) * eps)
delta = np.maximum(g_tol, np.max(np.abs(X[xk_in])) * eps)
[Mdir, _, valid, _, _, _] = formquad(X[: nf + 1, :], F[: nf + 1, :], delta, xk_in, Model["np_max"], Model["Par"], 1)
if not valid:
[Mdir, mp] = bmpts(X[xk_in], Mdir, Low, Upp, delta, Model["Par"][2])
Expand Down Expand Up @@ -326,7 +326,7 @@ def pounders(Ffun, X_0, n, nf_max, g_tol, delta_0, m, Low, Upp, Prior=None, Opti
xk_in = nf # Change current center
# 4b. Update the trust-region radius:
if (rho >= eta_1) and (step_norm > delta_inact * delta):
delta = min(delta * gamma_inc, delta_max)
delta = np.minimum(delta * gamma_inc, delta_max)
elif valid:
delta = delta * gamma_dec
if delta <= delta_min:
Expand Down
1 change: 1 addition & 0 deletions pounders/py/tests/test_emittance_opt.py
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Expand Up @@ -14,6 +14,7 @@ def call_beamline_simulation(x):
return np.squeeze(out)


X_0 = np.random.seed(8675309)
# Adjust these:
n = 4 # Number of parameters to be optimized
X_0 = np.random.uniform(0, 1, (1, n)) # starting parameters for the optimizer
Expand Down
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