From 0e4f2a3798c8c2503c3dccb2a00d3061540bb259 Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Thu, 5 Mar 2026 18:08:07 -0800
Subject: [PATCH 01/13] Added convergence flag to fit()

---
 src/diffpy/stretched_nmf/snmf_class.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index d5f6603..566e965 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -210,7 +210,8 @@ def fit(self, rho=0, eta=0, reset=True):
             the output of the previous
             fit() as their input.
         """
-
+        self.converged_ = False
+        
         if reset:
             self.components_ = self.init_components.copy()
             self.weights_ = self.init_weights.copy()
@@ -294,6 +295,7 @@ def fit(self, rho=0, eta=0, reset=True):
                 self.objective_difference < self.objective_function * self.tol
                 and outiter >= self.min_iter
             ):
+                self.converged_ = True
                 break
 
         self.normalize_results()

From c2a825fb1dcb07bf7deadb1da0f8dd0378e81cd9 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 6 Mar 2026 02:14:09 +0000
Subject: [PATCH 02/13] [pre-commit.ci] auto fixes from pre-commit hooks

---
 src/diffpy/stretched_nmf/snmf_class.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 566e965..f96c854 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -211,7 +211,7 @@ def fit(self, rho=0, eta=0, reset=True):
             fit() as their input.
         """
         self.converged_ = False
-        
+
         if reset:
             self.components_ = self.init_components.copy()
             self.weights_ = self.init_weights.copy()

From fe8102411aacbf4e09d22d2bd8afbe5c50c5cd9c Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 09:17:56 -0800
Subject: [PATCH 03/13] Added news

---
 news/add-convergence-flag.rst | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 news/add-convergence-flag.rst

diff --git a/news/add-convergence-flag.rst b/news/add-convergence-flag.rst
new file mode 100644
index 0000000..e4776b7
--- /dev/null
+++ b/news/add-convergence-flag.rst
@@ -0,0 +1,25 @@
+**Added:**
+
+* SNMFOptimizer.converged_ attribute to indicate whether the optimization
+  successfully reached the convergence tolerance (True) or stopped because the
+  maximum number of iterations was reached (False).
+
+**Changed:**
+
+* <news item>
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* <news item>
+
+**Fixed:**
+
+* <news item>
+
+**Security:**
+
+* <news item>

From f24c5e3b8e6cbcec134b0c201933b1f999014718 Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Fri, 6 Mar 2026 09:36:50 -0800
Subject: [PATCH 04/13] Update snmf_class.py

Was giving me errors due to use  of python 3.11
---
 src/diffpy/stretched_nmf/snmf_class.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index f96c854..35692b9 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -252,11 +252,12 @@ def fit(self, rho=0, eta=0, reset=True):
         sparsity_term = self.eta * np.sum(
             np.sqrt(self.components_)
         )  # Square root penalty
+        obj_diff = (
+            self.objective_function - regularization_term - sparsity_term
+        )
         print(
             f"Start, Objective function: {self.objective_function:.5e}"
-            f", Obj - reg/sparse: {self.objective_function
-                                   - regularization_term
-                                   - sparsity_term:.5e}"
+            f", Obj - reg/sparse: {obj_diff:.5e}"
         )
 
         # Main optimization loop
@@ -275,11 +276,12 @@ def fit(self, rho=0, eta=0, reset=True):
             sparsity_term = self.eta * np.sum(
                 np.sqrt(self.components_)
             )  # Square root penalty
+            obj_diff = (
+                self.objective_function - regularization_term - sparsity_term
+            )
             print(
                 f"Obj fun: {self.objective_function:.5e}, "
-                f"Obj - reg/sparse: {self.objective_function
-                                     - regularization_term
-                                     - sparsity_term:.5e}, "
+                f", Obj - reg/sparse: {obj_diff:.5e}"
                 f"Iter: {self.outiter}"
             )
 

From 7eca89d3f7acd604a3eefc68ff9d51a7739abecc Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Thu, 12 Mar 2026 11:14:39 -0700
Subject: [PATCH 05/13] Update snmf_class.py

Optimized print messages
Added verbose option
Added objective_log attribute to track objective function updates, with associated time stamps and specifying what matrix has been updated
---
 src/diffpy/stretched_nmf/snmf_class.py | 120 ++++++++++++++++++-------
 1 file changed, 86 insertions(+), 34 deletions(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 35692b9..1270bf2 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -1,3 +1,5 @@
+import time
+
 import cvxpy as cp
 import numpy as np
 from scipy.optimize import minimize
@@ -82,6 +84,7 @@ def __init__(
         n_components=None,
         random_state=None,
         show_plots=False,
+        verbose=True,
     ):
         """Initialize an instance of sNMF.
 
@@ -131,6 +134,7 @@ def __init__(
         self.num_updates = 0
         self._rng = np.random.default_rng(random_state)
         self.plotter = SNMFPlotter() if show_plots else None
+        self.verbose = verbose
 
         # Enforce exclusive specification of n_components or init_weights
         if (n_components is None and init_weights is None) or (
@@ -183,6 +187,7 @@ def __init__(
             [1, -2, 1],
             offsets=[0, 1, 2],
             shape=(self.n_signals - 2, self.n_signals),
+            dtype=float,
         )
 
     def fit(self, rho=0, eta=0, reset=True):
@@ -235,6 +240,13 @@ def fit(self, rho=0, eta=0, reset=True):
         ]
         self.objective_difference = None
         self._objective_history = [self.objective_function]
+        self.objective_log = [
+            {
+                "step": "start",
+                "objective": self.objective_function,
+                "timestamp": time.time(),
+            }
+        ]
 
         # Set up tracking variables for update_components()
         self._prev_components = None
@@ -255,10 +267,11 @@ def fit(self, rho=0, eta=0, reset=True):
         obj_diff = (
             self.objective_function - regularization_term - sparsity_term
         )
-        print(
-            f"Start, Objective function: {self.objective_function:.5e}"
-            f", Obj - reg/sparse: {obj_diff:.5e}"
-        )
+        if self.verbose:
+            print(
+                f"Start, Objective function: {self.objective_function:.5e}"
+                f", Obj - reg/sparse: {obj_diff:.5e}"
+            )
 
         # Main optimization loop
         for outiter in range(self.max_iter):
@@ -279,22 +292,19 @@ def fit(self, rho=0, eta=0, reset=True):
             obj_diff = (
                 self.objective_function - regularization_term - sparsity_term
             )
-            print(
-                f"Obj fun: {self.objective_function:.5e}, "
-                f", Obj - reg/sparse: {obj_diff:.5e}"
-                f"Iter: {self.outiter}"
-            )
-
+            convergence_threshold = self.objective_function * self.tol
             # Convergence check: Stop if diffun is small
             # and at least min_iter iterations have passed
-            print(
-                "Checking if ",
-                self.objective_difference,
-                " < ",
-                self.objective_function * self.tol,
-            )
+            if self.verbose:
+                print(
+                    f"\n--- Iteration {self.outiter} ---"
+                    f"\nTotal Objective   : {self.objective_function:.5e}"
+                    f"\nBase Obj (No Reg) : {obj_diff:.5e}"
+                    f"\nConvergence Check : Δ {self.objective_difference:.5e}"
+                    f" < {convergence_threshold:.5e} (Threshold)\n"
+                )
             if (
-                self.objective_difference < self.objective_function * self.tol
+                self.objective_difference < convergence_threshold
                 and outiter >= self.min_iter
             ):
                 self.converged_ = True
@@ -305,6 +315,8 @@ def fit(self, rho=0, eta=0, reset=True):
         return self
 
     def normalize_results(self):
+        if self.verbose:
+            print("\nNormalizing results after convergence...")
         # Select our best results for normalization
         self.components_ = self.best_matrices[0]
         self.weights_ = self.best_matrices[1]
@@ -335,11 +347,18 @@ def normalize_results(self):
             self.update_components()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            print(
-                f"Objective function after normalize_components: "
-                f"{self.objective_function:.5e}"
-            )
+            # print(
+            #     f"Objective function after normalize_components: "
+            #     f"{self.objective_function:.5e}"
+            # )
             self._objective_history.append(self.objective_function)
+            self.objective_log = [
+                {
+                    "step": "c_norm",
+                    "objective": self.objective_function,
+                    "timestamp": time.time(),
+                }
+            ]
             self.objective_difference = (
                 self._objective_history[-2] - self._objective_history[-1]
             )
@@ -357,16 +376,25 @@ def normalize_results(self):
                 break
 
     def outer_loop(self):
+        if self.verbose:
+            print("Updating components and weights in outer loop...")
         for iter in range(4):
             self.iter = iter
             self._prev_grad_components = self._grad_components.copy()
             self.update_components()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            print(
-                f"Objective function after update_components: "
-                f"{self.objective_function:.5e}"
-            )
+            self.objective_log = [
+                {
+                    "step": "c",
+                    "objective": self.objective_function,
+                    "timestamp": time.time(),
+                }
+            ]
+            # print(
+            #     f"Objective function after update_components: "
+            #     f"{self.objective_function:.5e}"
+            # )
             self._objective_history.append(self.objective_function)
             self.objective_difference = (
                 self._objective_history[-2] - self._objective_history[-1]
@@ -389,11 +417,19 @@ def outer_loop(self):
             self.update_weights()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            print(
-                f"Objective function after update_weights: "
-                f"{self.objective_function:.5e}"
-            )
+            # print(
+            #     f"Objective function after update_weights: "
+            #     f"{self.objective_function:.5e}"
+            # )
             self._objective_history.append(self.objective_function)
+            self.objective_log = [
+                {
+                    "step": "w",
+                    "objective": self.objective_function,
+                    "timestamp": time.time(),
+                }
+            ]
+
             self.objective_difference = (
                 self._objective_history[-2] - self._objective_history[-1]
             )
@@ -426,11 +462,18 @@ def outer_loop(self):
             self.update_stretch()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            print(
-                f"Objective function after update_stretch: "
-                f"{self.objective_function:.5e}"
-            )
+            # print(
+            #     f"Objective function after update_stretch: "
+            #     f"{self.objective_function:.5e}"
+            # )
             self._objective_history.append(self.objective_function)
+            self.objective_log = [
+                {
+                    "step": "s",
+                    "objective": self.objective_function,
+                    "timestamp": time.time(),
+                }
+            ]
             self.objective_difference = (
                 self._objective_history[-2] - self._objective_history[-1]
             )
@@ -712,7 +755,12 @@ def solve_quadratic_program(self, t, m):
 
         # Solve using a QP solver
         prob = cp.Problem(objective, constraints)
-        prob.solve(solver=cp.OSQP, verbose=False)
+        prob.solve(
+            solver=cp.OSQP,
+            verbose=False,
+            polish=False,  # TODO keep? removes polish message
+            # solver_verbose=False
+        )
 
         # Get the solution
         return np.maximum(
@@ -722,6 +770,7 @@ def solve_quadratic_program(self, t, m):
     def update_components(self):
         """Updates `components` using gradient-based optimization with
         adaptive step size."""
+
         # Compute stretched components using the interpolation function
         stretched_components, _, _ = (
             self.compute_stretched_components()
@@ -868,6 +917,9 @@ def update_stretch(self):
         """Updates stretching matrix using constrained optimization
         (equivalent to fmincon in MATLAB)."""
 
+        if self.verbose:
+            print("Updating stretch factors...")
+
         # Flatten stretch for compatibility with the optimizer
         # (since SciPy expects 1D input)
         stretch_flat_initial = self.stretch_.flatten()

From d5a35687604c5a1f10769f0e072c70ebb90e9db4 Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Thu, 12 Mar 2026 11:26:41 -0700
Subject: [PATCH 06/13] Update snmf_class.py

---
 src/diffpy/stretched_nmf/snmf_class.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 1270bf2..3782673 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -269,8 +269,9 @@ def fit(self, rho=0, eta=0, reset=True):
         )
         if self.verbose:
             print(
-                f"Start, Objective function: {self.objective_function:.5e}"
-                f", Obj - reg/sparse: {obj_diff:.5e}"
+                f"\n--- Start ---"
+                f"\nTotal Objective   : {self.objective_function:.5e}"
+                f"\nBase Obj (No Reg) : {obj_diff:.5e}"
             )
 
         # Main optimization loop
@@ -300,8 +301,9 @@ def fit(self, rho=0, eta=0, reset=True):
                     f"\n--- Iteration {self.outiter} ---"
                     f"\nTotal Objective   : {self.objective_function:.5e}"
                     f"\nBase Obj (No Reg) : {obj_diff:.5e}"
-                    f"\nConvergence Check : Δ {self.objective_difference:.5e}"
-                    f" < {convergence_threshold:.5e} (Threshold)\n"
+                    "\nConvergence Check : Δ "
+                    f"({self.objective_difference:.2e})"
+                    f" < Threshold ({convergence_threshold:.2e})\n"
                 )
             if (
                 self.objective_difference < convergence_threshold

From 5662d605f283a5e444d222904b69bbeead925090 Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Thu, 12 Mar 2026 14:55:18 -0700
Subject: [PATCH 07/13] Update snmf_class.py

Fixed wrong setting of objective_log
---
 src/diffpy/stretched_nmf/snmf_class.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 3782673..2815707 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -354,13 +354,13 @@ def normalize_results(self):
             #     f"{self.objective_function:.5e}"
             # )
             self._objective_history.append(self.objective_function)
-            self.objective_log = [
+            self.objective_log.append(
                 {
                     "step": "c_norm",
                     "objective": self.objective_function,
                     "timestamp": time.time(),
                 }
-            ]
+            )
             self.objective_difference = (
                 self._objective_history[-2] - self._objective_history[-1]
             )
@@ -386,13 +386,13 @@ def outer_loop(self):
             self.update_components()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            self.objective_log = [
+            self.objective_log.append(
                 {
                     "step": "c",
                     "objective": self.objective_function,
                     "timestamp": time.time(),
                 }
-            ]
+            )
             # print(
             #     f"Objective function after update_components: "
             #     f"{self.objective_function:.5e}"
@@ -424,13 +424,13 @@ def outer_loop(self):
             #     f"{self.objective_function:.5e}"
             # )
             self._objective_history.append(self.objective_function)
-            self.objective_log = [
+            self.objective_log.append(
                 {
                     "step": "w",
                     "objective": self.objective_function,
                     "timestamp": time.time(),
                 }
-            ]
+            )
 
             self.objective_difference = (
                 self._objective_history[-2] - self._objective_history[-1]
@@ -469,13 +469,13 @@ def outer_loop(self):
             #     f"{self.objective_function:.5e}"
             # )
             self._objective_history.append(self.objective_function)
-            self.objective_log = [
+            self.objective_log.append(
                 {
                     "step": "s",
                     "objective": self.objective_function,
                     "timestamp": time.time(),
                 }
-            ]
+            )
             self.objective_difference = (
                 self._objective_history[-2] - self._objective_history[-1]
             )

From f69c1a16ea47650f96e77901639db720e776795d Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Thu, 12 Mar 2026 15:30:04 -0700
Subject: [PATCH 08/13] Update snmf_class.py

remove commented print messages
---
 src/diffpy/stretched_nmf/snmf_class.py | 16 ----------------
 1 file changed, 16 deletions(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 2815707..28ca6c7 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -349,10 +349,6 @@ def normalize_results(self):
             self.update_components()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            # print(
-            #     f"Objective function after normalize_components: "
-            #     f"{self.objective_function:.5e}"
-            # )
             self._objective_history.append(self.objective_function)
             self.objective_log.append(
                 {
@@ -393,10 +389,6 @@ def outer_loop(self):
                     "timestamp": time.time(),
                 }
             )
-            # print(
-            #     f"Objective function after update_components: "
-            #     f"{self.objective_function:.5e}"
-            # )
             self._objective_history.append(self.objective_function)
             self.objective_difference = (
                 self._objective_history[-2] - self._objective_history[-1]
@@ -419,10 +411,6 @@ def outer_loop(self):
             self.update_weights()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            # print(
-            #     f"Objective function after update_weights: "
-            #     f"{self.objective_function:.5e}"
-            # )
             self._objective_history.append(self.objective_function)
             self.objective_log.append(
                 {
@@ -464,10 +452,6 @@ def outer_loop(self):
             self.update_stretch()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            # print(
-            #     f"Objective function after update_stretch: "
-            #     f"{self.objective_function:.5e}"
-            # )
             self._objective_history.append(self.objective_function)
             self.objective_log.append(
                 {

From 90c81a8799b7e139471da22f0c3f5e403f6218d7 Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Thu, 12 Mar 2026 15:43:58 -0700
Subject: [PATCH 09/13] Update snmf_class.py

Added "iteration" key to objective_log
Removed redundant _objective_history attribute
---
 src/diffpy/stretched_nmf/snmf_class.py | 39 +++++++++++++++-----------
 1 file changed, 23 insertions(+), 16 deletions(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 28ca6c7..39a4324 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -239,10 +239,10 @@ def fit(self, rho=0, eta=0, reset=True):
             self.stretch_.copy(),
         ]
         self.objective_difference = None
-        self._objective_history = [self.objective_function]
         self.objective_log = [
             {
                 "step": "start",
+                "iteration": 0,
                 "objective": self.objective_function,
                 "timestamp": time.time(),
             }
@@ -340,7 +340,6 @@ def normalize_results(self):
         self.residuals = self.get_residual_matrix()
         self.objective_function = self.get_objective_function()
         self.objective_difference = None
-        self._objective_history = [self.objective_function]
         self.outiter = 0
         self.iter = 0
         for outiter in range(self.max_iter):
@@ -349,16 +348,17 @@ def normalize_results(self):
             self.update_components()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            self._objective_history.append(self.objective_function)
             self.objective_log.append(
                 {
                     "step": "c_norm",
+                    "iteration": outiter,
                     "objective": self.objective_function,
                     "timestamp": time.time(),
                 }
             )
             self.objective_difference = (
-                self._objective_history[-2] - self._objective_history[-1]
+                self.objective_log[-2]["objective"]
+                - self.objective_log[-1]["objective"]
             )
             if self.plotter is not None:
                 self.plotter.update(
@@ -385,13 +385,14 @@ def outer_loop(self):
             self.objective_log.append(
                 {
                     "step": "c",
+                    "iteration": self.outiter,
                     "objective": self.objective_function,
                     "timestamp": time.time(),
                 }
             )
-            self._objective_history.append(self.objective_function)
             self.objective_difference = (
-                self._objective_history[-2] - self._objective_history[-1]
+                self.objective_log[-2]["objective"]
+                - self.objective_log[-1]["objective"]
             )
             if self.objective_function < self.best_objective:
                 self.best_objective = self.objective_function
@@ -411,17 +412,18 @@ def outer_loop(self):
             self.update_weights()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            self._objective_history.append(self.objective_function)
             self.objective_log.append(
                 {
                     "step": "w",
+                    "iteration": self.outiter,
                     "objective": self.objective_function,
                     "timestamp": time.time(),
                 }
             )
 
             self.objective_difference = (
-                self._objective_history[-2] - self._objective_history[-1]
+                self.objective_log[-2]["objective"]
+                - self.objective_log[-1]["objective"]
             )
             if self.objective_function < self.best_objective:
                 self.best_objective = self.objective_function
@@ -439,10 +441,11 @@ def outer_loop(self):
                 )
 
             self.objective_difference = (
-                self._objective_history[-2] - self._objective_history[-1]
+                self.objective_log[-2]["objective"]
+                - self.objective_log[-1]["objective"]
             )
             if (
-                self._objective_history[-3] - self.objective_function
+                self.objective_log[-3]["objective"] - self.objective_function
                 < self.objective_difference * 1e-3
             ):
                 break
@@ -452,16 +455,17 @@ def outer_loop(self):
             self.update_stretch()
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
-            self._objective_history.append(self.objective_function)
             self.objective_log.append(
                 {
                     "step": "s",
+                    "iteration": self.outiter,
                     "objective": self.objective_function,
                     "timestamp": time.time(),
                 }
             )
             self.objective_difference = (
-                self._objective_history[-2] - self._objective_history[-1]
+                self.objective_log[-2]["objective"]
+                - self.objective_log[-1]["objective"]
             )
             if self.objective_function < self.best_objective:
                 self.best_objective = self.objective_function
@@ -820,10 +824,13 @@ def update_components(self):
             )
             self.components_ = mask * self.components_
 
-            objective_improvement = self._objective_history[
-                -1
-            ] - self.get_objective_function(
-                residuals=self.get_residual_matrix()
+            # self.objective_function is the baseline value cached right
+            # before we called update_components()
+            objective_improvement = (
+                self.objective_function
+                - self.get_objective_function(
+                    residuals=self.get_residual_matrix()
+                )
             )
 
             # Check if objective function improves

From 128cb5baba5239deb0180506b5787b6239466cf5 Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Thu, 12 Mar 2026 15:52:50 -0700
Subject: [PATCH 10/13] Create optimize-messages.rst

---
 news/optimize-messages.rst | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 news/optimize-messages.rst

diff --git a/news/optimize-messages.rst b/news/optimize-messages.rst
new file mode 100644
index 0000000..6ec132f
--- /dev/null
+++ b/news/optimize-messages.rst
@@ -0,0 +1,32 @@
+**Added:**
+
+* 'SNMFOptimizer.objective_log' attr: dictionary list to track the optimization
+  process, recording the step, iteration, objective, and timestamp at each update.
+  Uses the 'step', 'iteration', 'objective' and 'timestamp' keys.
+* 'SNMFOptimizer(verbose : Optional[bool])' option and SNMFOptimizer.verbose
+  attribute to allow users to toggle diagnostic console output.
+
+**Changed:**
+
+* Modified all print messages for improved readability and tied them to the new
+  verbose flag.
+* Refactored convergence checks and step-size calculations to pull objective
+  values directly from objective_log instead of relying on a separate history
+  array.
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* Removed the 'SNMFOptimizer._objective_history' list, which was made redundant
+  by the comprehensive 'SNMFOptimizer.objective_log' tracking system.
+
+**Fixed:**
+
+* <news item>
+
+**Security:**
+
+* <news item>

From 8ea3998837b8eca1c73bb4dac091d544c1e55866 Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Fri, 13 Mar 2026 11:35:51 -0700
Subject: [PATCH 11/13] Update snmf_class.py

---
 src/diffpy/stretched_nmf/snmf_class.py | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 39a4324..0af3e2e 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -264,14 +264,14 @@ def fit(self, rho=0, eta=0, reset=True):
         sparsity_term = self.eta * np.sum(
             np.sqrt(self.components_)
         )  # Square root penalty
-        obj_diff = (
+        base_obj = (
             self.objective_function - regularization_term - sparsity_term
         )
         if self.verbose:
             print(
                 f"\n--- Start ---"
                 f"\nTotal Objective   : {self.objective_function:.5e}"
-                f"\nBase Obj (No Reg) : {obj_diff:.5e}"
+                f"\nBase Obj (No Reg) : {base_obj:.5e}"
             )
 
         # Main optimization loop
@@ -290,7 +290,7 @@ def fit(self, rho=0, eta=0, reset=True):
             sparsity_term = self.eta * np.sum(
                 np.sqrt(self.components_)
             )  # Square root penalty
-            obj_diff = (
+            base_obj = (
                 self.objective_function - regularization_term - sparsity_term
             )
             convergence_threshold = self.objective_function * self.tol
@@ -300,7 +300,7 @@ def fit(self, rho=0, eta=0, reset=True):
                 print(
                     f"\n--- Iteration {self.outiter} ---"
                     f"\nTotal Objective   : {self.objective_function:.5e}"
-                    f"\nBase Obj (No Reg) : {obj_diff:.5e}"
+                    f"\nBase Obj (No Reg) : {base_obj:.5e}"
                     "\nConvergence Check : Δ "
                     f"({self.objective_difference:.2e})"
                     f" < Threshold ({convergence_threshold:.2e})\n"
@@ -367,15 +367,24 @@ def normalize_results(self):
                     stretch=self.stretch_,
                     update_tag="normalize components",
                 )
+            convergence_threshold = self.objective_function * self.tol
+            if self.verbose:
+                print(
+                    f"\n--- Iteration {outiter} after normalization---"
+                    f"\nTotal Objective   : {self.objective_function:.5e}"
+                    "\nConvergence Check : Δ "
+                    f"({self.objective_difference:.2e})"
+                    f" < Threshold ({convergence_threshold:.2e})\n"
+                )
             if (
-                self.objective_difference < self.objective_function * self.tol
+                self.objective_difference < convergence_threshold
                 and outiter >= 7
             ):
                 break
 
     def outer_loop(self):
         if self.verbose:
-            print("Updating components and weights in outer loop...")
+            print("Updating components and weights...")
         for iter in range(4):
             self.iter = iter
             self._prev_grad_components = self._grad_components.copy()

From ab71e11dc0862a325f1d64b3c263858f898f996b Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Fri, 13 Mar 2026 12:36:23 -0700
Subject: [PATCH 12/13] Update snmf_class.py

---
 src/diffpy/stretched_nmf/snmf_class.py | 55 ++++++++++++++------------
 1 file changed, 29 insertions(+), 26 deletions(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 1e77c0f..9155b05 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -218,6 +218,7 @@ def _initialize_factors(
             [1, -2, 1],
             offsets=[0, 1, 2],
             shape=(self.n_signals_ - 2, self.n_signals_),
+            dtype=float,
         )
 
     def fit(
@@ -306,12 +307,12 @@ def fit(
             self.weights_.copy(),
             self.stretch_.copy(),
         ]
-        self.objective_difference = None
+        self.objective_difference_ = None
         self.objective_log = [
             {
                 "step": "start",
                 "iteration": 0,
-                "objective": self.objective_function,
+                "objective": self.objective_function_,
                 "timestamp": time.time(),
             }
         ]
@@ -334,12 +335,12 @@ def fit(
             np.sqrt(self.components_)
         )  # Square root penalty
         base_obj = (
-            self.objective_function - regularization_term - sparsity_term
+            self.objective_function_ - regularization_term - sparsity_term
         )
         if self.verbose:
             print(
                 f"\n--- Start ---"
-                f"\nTotal Objective   : {self.objective_function:.5e}"
+                f"\nTotal Objective   : {self.objective_function_:.5e}"
                 f"\nBase Obj (No Reg) : {base_obj:.5e}"
             )
 
@@ -361,22 +362,22 @@ def fit(
                 np.sqrt(self.components_)
             )  # Square root penalty
             base_obj = (
-                self.objective_function - regularization_term - sparsity_term
+                self.objective_function_ - regularization_term - sparsity_term
             )
-            convergence_threshold = self.objective_function * self.tol
+            convergence_threshold = self.objective_function_ * self.tol
             # Convergence check: Stop if diffun is small
             # and at least min_iter iterations have passed
             if self.verbose:
                 print(
-                    f"\n--- Iteration {self.outiter} ---"
-                    f"\nTotal Objective   : {self.objective_function:.5e}"
+                    f"\n--- Iteration {self._outer_iter} ---"
+                    f"\nTotal Objective   : {self.objective_function_:.5e}"
                     f"\nBase Obj (No Reg) : {base_obj:.5e}"
                     "\nConvergence Check : Δ "
-                    f"({self.objective_difference:.2e})"
+                    f"({self.objective_difference_:.2e})"
                     f" < Threshold ({convergence_threshold:.2e})\n"
                 )
             if (
-                self.objective_difference < convergence_threshold
+                self.objective_difference_ < convergence_threshold
                 and outiter >= self.min_iter
             ):
                 self.converged_ = True
@@ -427,13 +428,14 @@ def _normalize_results(self):
                 {
                     "step": "c_norm",
                     "iteration": outiter,
-                    "objective": self.objective_function,
+                    "objective": self.objective_function_,
                     "timestamp": time.time(),
                 }
             )
-            self.objective_difference = (
+            self.objective_difference_ = (
                 self.objective_log[-2]["objective"]
                 - self.objective_log[-1]["objective"]
+            )
             if self._plotter is not None:
                 self._plotter.update(
                     components=self.components_,
@@ -441,13 +443,13 @@ def _normalize_results(self):
                     stretch=self.stretch_,
                     update_tag="normalize components",
                 )
-            convergence_threshold = self.objective_function * self.tol
+            convergence_threshold = self.objective_function_ * self.tol
             if self.verbose:
                 print(
                     f"\n--- Iteration {outiter} after normalization---"
-                    f"\nTotal Objective   : {self.objective_function:.5e}"
+                    f"\nTotal Objective   : {self.objective_function_:.5e}"
                     "\nConvergence Check : Δ "
-                    f"({self.objective_difference:.2e})"
+                    f"({self.objective_difference_:.2e})"
                     f" < Threshold ({convergence_threshold:.2e})\n"
                 )
             if (
@@ -468,12 +470,12 @@ def _outer_loop(self):
             self.objective_log.append(
                 {
                     "step": "c",
-                    "iteration": self.outiter,
-                    "objective": self.objective_function,
+                    "iteration": self._outer_iter,
+                    "objective": self.objective_function_,
                     "timestamp": time.time(),
                 }
             )
-            self.objective_difference = (
+            self.objective_difference_ = (
                 self.objective_log[-2]["objective"]
                 - self.objective_log[-1]["objective"]
             )
@@ -498,13 +500,13 @@ def _outer_loop(self):
             self.objective_log.append(
                 {
                     "step": "w",
-                    "iteration": self.outiter,
-                    "objective": self.objective_function,
+                    "iteration": self._outer_iter,
+                    "objective": self.objective_function_,
                     "timestamp": time.time(),
                 }
             )
 
-            self.objective_difference = (
+            self.objective_difference_ = (
                 self.objective_log[-2]["objective"]
                 - self.objective_log[-1]["objective"]
             )
@@ -541,12 +543,12 @@ def _outer_loop(self):
             self.objective_log.append(
                 {
                     "step": "s",
-                    "iteration": self.outiter,
-                    "objective": self.objective_function,
+                    "iteration": self._outer_iter,
+                    "objective": self.objective_function_,
                     "timestamp": time.time(),
                 }
             )
-            self.objective_difference = (
+            self.objective_difference_ = (
                 self.objective_log[-2]["objective"]
                 - self.objective_log[-1]["objective"]
             )
@@ -914,8 +916,9 @@ def _update_components(self):
             )
             self.components_ = mask * self.components_
 
-            objective_improvement = self.objective_log[-1]["objective"]
-              - self._get_objective_function(
+            objective_improvement = self.objective_log[-1][
+                "objective"
+            ] - self._get_objective_function(
                 residuals=self._get_residual_matrix()
             )
 

From f504ce6243c17c339420e105ad4b37aa4412af35 Mon Sep 17 00:00:00 2001
From: Andrea-gm <162758982+Andrea-gm@users.noreply.github.com>
Date: Mon, 16 Mar 2026 09:18:00 -0700
Subject: [PATCH 13/13] Update snmf_class.py

---
 src/diffpy/stretched_nmf/snmf_class.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/diffpy/stretched_nmf/snmf_class.py b/src/diffpy/stretched_nmf/snmf_class.py
index 9155b05..3269570 100644
--- a/src/diffpy/stretched_nmf/snmf_class.py
+++ b/src/diffpy/stretched_nmf/snmf_class.py
@@ -84,7 +84,7 @@ def __init__(
         eta=0,
         random_state=None,
         show_plots=False,
-        verbose=True,
+        verbose=False,
     ):
         """Initialize an instance of sNMF with estimator
         hyperparameters.