diff --git a/experiments/cubic_sparse_logreg/runme.jl b/experiments/cubic_sparse_logreg/runme.jl
index 69d2d22..a50b8a9 100644
--- a/experiments/cubic_sparse_logreg/runme.jl
+++ b/experiments/cubic_sparse_logreg/runme.jl
@@ -66,61 +66,73 @@ function run_cubic_logreg_data(
     f = Cubic(Q, q, lam)
     g = ProximalCore.Zero()
 
+
+    x0 = zeros(n)
+    x_pert = x0 + randn(size(x0))
+
+    grad_x, _ = ProximalCore.gradient(f, x0)
+    grad_x_pert, _ = ProximalCore.gradient(f, x_pert)
+    gam_init = norm(x0 - x_pert)^2 / dot(grad_x - grad_x_pert, x0 - x_pert) 
+
     # run algorithm with 1/10 the tolerance to get "accurate" solution
     sol, numit = AdaProx.adaptive_proxgrad(
-        zeros(n),
+        x0,
         f = f,
         g = g,
-        rule = AdaProx.OurRule(gamma = 1.0),
+        rule = AdaProx.OurRule(gamma = gam_init),
         tol = tol / 10,
         maxit = maxit * 10,
         name = nothing,
     )
-
-    sol, numit = AdaProx.backtracking_proxgrad(
-        zeros(n),
-        f = AdaProx.Counting(f),
-        g = g,
-        gamma0 = 1.0,
-        tol = tol,
-        maxit = maxit,
-        name = "PGM (backtracking)",
-    )
+    xi_values = [1, 1.5, 2]
+    for xi = xi_values
+        sol, numit = AdaProx.backtracking_proxgrad(
+            x0,
+            f = AdaProx.Counting(f),
+            g = g,
+            gamma0 = gam_init,
+            xi = xi,
+            tol = tol,
+            maxit = maxit,
+            name = "PGM (backtracking)-(xi=$(xi))",
+        )
+    end
 
     sol, numit = AdaProx.backtracking_nesterov(
-        zeros(n),
+        x0,
         f = AdaProx.Counting(f),
         g = g,
-        gamma0 = 1.0,
+        gamma0 = gam_init,
         tol = tol,
         maxit = maxit,
         name = "Nesterov (backtracking)",
     )
 
     sol, numit = AdaProx.adaptive_proxgrad(
-        zeros(n),
+        x0,
         f = AdaProx.Counting(f),
         g = g,
-        rule = AdaProx.MalitskyMishchenkoRule(gamma = 1.0),
+        rule = AdaProx.MalitskyMishchenkoRule(gamma = gam_init),
         tol = tol,
         maxit = maxit,
         name = "AdaPGM (MM)",
     )
 
     sol, numit = AdaProx.adaptive_proxgrad(
-        zeros(n),
+        x0,
         f = AdaProx.Counting(f),
         g = g,
-        rule = AdaProx.OurRule(gamma = 1.0),
+        rule = AdaProx.OurRule(gamma = gam_init),
         tol = tol,
         maxit = maxit,
         name = "AdaPGM (Ours)",
     )
 
     sol, numit = AdaProx.agraal(
-        zeros(n),
+        x0,
         f = AdaProx.Counting(f),
         g = g,
+        gamma0 = gam_init,
         tol = tol,
         maxit = maxit,
         name = "aGRAAL"
diff --git a/experiments/lasso/runme.jl b/experiments/lasso/runme.jl
index f09f138..82088c5 100644
--- a/experiments/lasso/runme.jl
+++ b/experiments/lasso/runme.jl
@@ -90,7 +90,7 @@ function run_random_lasso(;
         gamma = gam_init,
         tol = tol,
         maxit = maxit,
-        name = "PGM (1/Lf)"
+        name = "PGM (fixed)"
     )
 
     xi_values = [1, 1.5, 2]
@@ -117,6 +117,17 @@ function run_random_lasso(;
         name = "Nesterov (backtracking)"
     )
 
+
+    sol, numit = AdaProx.fixed_nesterov(
+        zeros(n),
+        f = AdaProx.Counting(f),
+        g = g,
+        gamma = gam_init,
+        tol = tol,
+        maxit = maxit,
+        name = "Nesterov (fixed)"
+    )
+
     sol, numit = AdaProx.adaptive_proxgrad(
         zeros(n),
         f = AdaProx.Counting(f),
@@ -142,6 +153,7 @@ function run_random_lasso(;
         zeros(n),
         f = AdaProx.Counting(f),
         g = g,
+        gamma0 = gam_init,
         tol = tol,
         maxit = maxit,
         name = "aGRAAL"
diff --git a/experiments/sparse_logreg/runme.jl b/experiments/sparse_logreg/runme.jl
index 1923e2d..06317d5 100644
--- a/experiments/sparse_logreg/runme.jl
+++ b/experiments/sparse_logreg/runme.jl
@@ -56,12 +56,14 @@ function run_logreg_l1_data(
     Lf = norm(X1 * X1') / 4 / m
 
     x0 = zeros(n)
+
+    gam_init = 1.0 / Lf
     # run algorithm with 1/10 the tolerance to get "accurate" solution
     sol, numit = AdaProx.adaptive_proxgrad(
         x0,
         f = f,
         g = g,
-        rule = AdaProx.OurRule(gamma = 1.0),
+        rule = AdaProx.OurRule(gamma = gam_init),
         tol = tol / 10,
         maxit = maxit * 10,
         name = nothing
@@ -71,7 +73,7 @@ function run_logreg_l1_data(
         x0,
         f = AdaProx.Counting(f),
         g = g,
-        gamma = 1.0 / Lf,
+        gamma = gam_init,
         tol = tol,
         maxit = maxit,
         name = "PGM (1/Lf)"
@@ -83,7 +85,7 @@ function run_logreg_l1_data(
             zeros(n),
             f = AdaProx.Counting(f),
             g = g,
-            gamma0 = 5.0,
+            gamma0 = gam_init,
             xi = xi, #increase in stepsize
             tol = tol,
             maxit = maxit/2,
@@ -95,17 +97,26 @@ function run_logreg_l1_data(
         x0,
         f = AdaProx.Counting(f),
         g = g,
-        gamma0 = 5.0,
+        gamma0 = gam_init,
         tol = tol,
         maxit = maxit/2,
         name = "Nesterov (backtracking)"
     )
+    sol, numit = AdaProx.fixed_nesterov(
+        x0,
+        f = AdaProx.Counting(f),
+        g = g,
+        gamma = gam_init,
+        tol = tol,
+        maxit = maxit/2,
+        name = "Nesterov (fixed)"
+    )
 
     sol, numit = AdaProx.adaptive_proxgrad(
         x0,
         f = AdaProx.Counting(f),
         g = g,
-        rule = AdaProx.MalitskyMishchenkoRule(gamma = 1.0),
+        rule = AdaProx.MalitskyMishchenkoRule(gamma = gam_init),
         tol = tol,
         maxit = maxit,
         name = "AdaPGM (MM)"
@@ -115,7 +126,7 @@ function run_logreg_l1_data(
         x0,
         f = AdaProx.Counting(f),
         g = g,
-        rule = AdaProx.OurRule(gamma = 1.0),
+        rule = AdaProx.OurRule(gamma = gam_init),
         tol = tol,
         maxit = maxit,
         name = "AdaPGM (Ours)"
@@ -125,6 +136,7 @@ function run_logreg_l1_data(
         x0,
         f = AdaProx.Counting(f),
         g = g,
+        gamma0 = gam_init,
         tol = tol,
         maxit = maxit,
         name = "aGRAAL"