Update defaults and docstrings

src/algorithms/ctmrg.jl

@@ -9,15 +9,17 @@ The projector bond dimensions are set via `trscheme` which controls the truncati
 properties inside of `TensorKit.tsvd`. Each CTMRG run is converged up to `tol`
 where the singular value convergence of the corners as well as the norm is checked.
 The maximal and minimal number of CTMRG iterations is set with `maxiter` and `miniter`.
-Different levels of output information are printed depending on `verbosity` (0, 1 or 2).
+Different levels of output information are printed depending on `verbosity`, where
+`0` is silent, `1` only prints warnings, `2` prints start and end of the run, and `3`
+displays information each iteration.
 Regardless of the truncation scheme, the space can be kept fixed with `fixedspace`.
 """
 @kwdef struct CTMRG
     trscheme::TruncationScheme = TensorKit.notrunc()
     tol::Float64 = Defaults.ctmrg_tol
     maxiter::Int = Defaults.ctmrg_maxiter
     miniter::Int = Defaults.ctmrg_miniter
-    verbosity::Int = 0
+    verbosity::Int = 1
     fixedspace::Bool = false
 end
 

src/algorithms/peps_opt.jl

@@ -40,7 +40,9 @@ based on the CTMRG gradient and updates the PEPS parameters. In this optimizatio
 the CTMRG runs can be started on the converged environments of the previous optimizer
 step by setting `reuse_env` to true. Otherwise a random environment is used at each
 step. The CTMRG gradient itself is computed using the `gradient_alg` algorithm.
-Different levels of output verbosity can be activated using `verbosity` (0, 1 or 2).
+Different levels of output information are printed depending on `verbosity`, where
+`0` is silent, `1` only prints warnings, `2` prints start and end of the run, and `3`
+displays information each iteration.
 """
 @kwdef struct PEPSOptimize{G}
     boundary_alg::CTMRG = CTMRG()  # Algorithm to find boundary environment
@@ -49,7 +51,7 @@ Different levels of output verbosity can be activated using `verbosity` (0, 1 or
     )
     reuse_env::Bool = true  # Reuse environment of previous optimization as initial guess for next
     gradient_alg::G = GeomSum() # Algorithm to solve gradient linear problem
-    verbosity::Int = 0
+    verbosity::Int = 1 # currently unused?
 end
 
 """