Final final fixes (#234)

* fix DDMRG test

* Fix typo

* tweak test to stabilize

Author: lkdvos

docs/src/examples/classic2d/1.hard-hexagon/index.md

@@ -64,7 +64,7 @@ F = 0.8839037051703857	S = 0.546862287635581	ξ = 13.8496825856899
 
 ## The scaling hypothesis
 
-The dominant eigenvector is of course only an approximation. The finite bond dimension enforces a finite correlation length, which effectively introduces a length scale in the system. This can be exploited to formulate a [pollmann2009](@cite), which in turn allows to extract the central charge.
+The dominant eigenvector is of course only an approximation. The finite bond dimension enforces a finite correlation length, which effectively introduces a length scale in the system. This can be exploited to formulate a scaling hypothesis [pollmann2009](@cite), which in turn allows to extract the central charge.
 
 First we need to know the entropy and correlation length at a bunch of different bond dimensions. Our approach will be to re-use the previous approximated dominant eigenvector, and then expanding its bond dimension and re-running VUMPS.
 According to the scaling hypothesis we should have ``S \propto \frac{c}{6} log(ξ)``. Therefore we should find ``c`` using

docs/src/examples/classic2d/1.hard-hexagon/main.ipynb

@@ -86,7 +86,7 @@
    "source": [
     "## The scaling hypothesis\n",
     "\n",
-    "The dominant eigenvector is of course only an approximation. The finite bond dimension enforces a finite correlation length, which effectively introduces a length scale in the system. This can be exploited to formulate a [pollmann2009](@cite), which in turn allows to extract the central charge.\n",
+    "The dominant eigenvector is of course only an approximation. The finite bond dimension enforces a finite correlation length, which effectively introduces a length scale in the system. This can be exploited to formulate a scaling hypothesis [pollmann2009](@cite), which in turn allows to extract the central charge.\n",
     "\n",
     "First we need to know the entropy and correlation length at a bunch of different bond dimensions. Our approach will be to re-use the previous approximated dominant eigenvector, and then expanding its bond dimension and re-running VUMPS.\n",
     "According to the scaling hypothesis we should have $S \\propto \\frac{c}{6} log(ξ)$. Therefore we should find $c$ using"

examples/classic2d/1.hard-hexagon/main.jl

@@ -51,7 +51,7 @@ println("F = $F\tS = $S\tξ = $ξ")
 md"""
 ## The scaling hypothesis
 
-The dominant eigenvector is of course only an approximation. The finite bond dimension enforces a finite correlation length, which effectively introduces a length scale in the system. This can be exploited to formulate a [pollmann2009](@cite), which in turn allows to extract the central charge.
+The dominant eigenvector is of course only an approximation. The finite bond dimension enforces a finite correlation length, which effectively introduces a length scale in the system. This can be exploited to formulate a scaling hypothesis [pollmann2009](@cite), which in turn allows to extract the central charge.
 
 First we need to know the entropy and correlation length at a bunch of different bond dimensions. Our approach will be to re-use the previous approximated dominant eigenvector, and then expanding its bond dimension and re-running VUMPS.
 According to the scaling hypothesis we should have ``S \propto \frac{c}{6} log(ξ)``. Therefore we should find ``c`` using

test/algorithms.jl

@@ -596,27 +596,20 @@ end
 end
 
 @testset "Dynamical DMRG" verbose = true begin
-    ham = force_planar(-1.0 * transverse_field_ising(; g=-4.0))
-    gs, = find_groundstate(InfiniteMPS([ℙ^2], [ℙ^10]), ham, VUMPS(; verbosity=0))
-    window = WindowMPS(gs, copy.([gs.AC[1]; [gs.AR[i] for i in 2:10]]), gs)
-
-    szd = force_planar(S_z())
-    @test [expectation_value(gs, i => szd) for i in 1:length(window)] ≈
-          [expectation_value(window, i => szd) for i in 1:length(window)] atol = 1e-10
-
-    openham = open_boundary_conditions(ham, length(window.window))
-    polepos = expectation_value(window.window, openham,
-                                environments(window.window, openham))
+    L = 10
+    H = force_planar(-transverse_field_ising(; L, g=-4))
+    gs, = find_groundstate(FiniteMPS(L, ℙ^2, ℙ^10), H; verbosity=verbosity_conv)
+    E₀ = expectation_value(gs, H)
 
-    vals = (-0.5:0.2:0.5) .+ polepos
+    vals = (-0.5:0.2:0.5) .+ E₀
     eta = 0.3im
 
-    predicted = [1 / (v + eta - polepos) for v in vals]
+    predicted = [1 / (v + eta - E₀) for v in vals]
 
     @testset "Flavour $f" for f in (Jeckelmann(), NaiveInvert())
         alg = DynamicalDMRG(; flavour=f, verbosity=0, tol=1e-8)
         data = map(vals) do v
-            result, = propagator(window.window, v + eta, openham, alg)
+            result, = propagator(gs, v + eta, H, alg)
             return result
         end
         @test data ≈ predicted atol = 1e-8
@@ -696,7 +689,7 @@ end
         H = force_planar(repeat(transverse_field_ising(; g=4), 2))
 
         dt = 1e-3
-        sW1 = make_time_mpo(H, dt, TaylorCluster(; N=3))
+        sW1 = make_time_mpo(H, dt, TaylorCluster(; N=3, compression=true, extension=true))
         sW2 = make_time_mpo(H, dt, WII())
         W1 = MPSKit.DenseMPO(sW1)
         W2 = MPSKit.DenseMPO(sW2)