remove useless assignments

Author: aldma

src/algorithms/drls.jl

@@ -147,8 +147,7 @@ update_direction_state!(iter::DRLSIteration, state::DRLSState) =
 
 function Base.iterate(iter::DRLSIteration{R,Tx,Tf}, state::DRLSState) where {R,Tx,Tf}
     # retrieve merit and set threshold
-    DRE_x = state.merit
-    threshold = iter.dre_sign * DRE_x - iter.c / iter.gamma * norm(state.res)^2
+    threshold = iter.dre_sign * state.merit - iter.c / iter.gamma * norm(state.res)^2
 
     set_next_direction!(iter, state)
 

src/algorithms/panoc.jl

@@ -193,10 +193,9 @@ function Base.iterate(iter::PANOCIteration{R,Tx,Tf}, state::PANOCState) where {R
     state.f_Ax = state.f_Ax_d
 
     # retrieve merit and set threshold
-    FBE_x = state.merit
     sigma = iter.beta * (0.5 / state.gamma) * (1 - iter.alpha)
-    tol = 10 * eps(R) * (1 + abs(FBE_x))
-    threshold = FBE_x - sigma * norm(state.res)^2 + tol
+    tol = 10 * eps(R) * (1 + abs(state.merit))
+    threshold = state.merit - sigma * norm(state.res)^2 + tol
 
     state.y .= state.x .- state.gamma .* state.At_grad_f_Ax
     state.g_z = prox!(state.z, iter.g, state.y, state.gamma)

src/algorithms/panocplus.jl

@@ -175,10 +175,10 @@ function Base.iterate(iter::PANOCplusIteration{R}, state::PANOCplusState) where
     state.res_prev .= state.res
 
     # retrieve merit and set threshold
-    FBE_x = state.merit
     sigma = iter.beta * (0.5 / state.gamma) * (1 - iter.alpha)
-    tol = 10 * eps(R) * (1 + abs(FBE_x))
-    threshold = FBE_x - sigma * norm(state.res)^2 + tol
+    tol = 10 * eps(R) * (1 + abs(state.merit))
+    threshold = state.merit - sigma * norm(state.res)^2 + tol
+    FBE_x = f_model(iter, state) + state.g_z
 
     tau_backtracks = 0
     can_update_direction = true

src/algorithms/zerofpr.jl

@@ -193,10 +193,10 @@ function Base.iterate(iter::ZeroFPRIteration{R}, state::ZeroFPRState) where {R}
     mul!(state.Ad, iter.A, state.d)
 
     # retrieve merit and set threshold
-    FBE_x = state.merit
     sigma = iter.beta * (0.5 / state.gamma) * (1 - iter.alpha)
-    tol = 10 * eps(R) * (1 + abs(FBE_x))
-    threshold = FBE_x - sigma * norm(state.res)^2 + tol
+    tol = 10 * eps(R) * (1 + abs(state.merit))
+    threshold = state.merit - sigma * norm(state.res)^2 + tol
+    FBE_x = f_model(iter, state) + state.g_xbar
 
     for k = 1:iter.max_backtracks
         state.x .= state.xbar_prev .+ state.tau .* state.d