Add adaptive tolerance scaling for boundary and gradient algorithms in variational optimization

[leburgel]

Adds adaptive tolerance scaling, where the tolerance of the contraction algorithm is set based on the current norm of the gradient (default scaling factor of 1.0e-3). The tolerance of the gradient algorithm in turn is set based on the contraction tolerance (default scaling factor 1.0e1).

On the README example, these default settings give a 1.5x speedup in walltime needed to reach a given energy value starting from the same initial guess. I'll see what the tests say for now, and do some more trials in the meantime.

At the same time, this reworks the way the per-iteration info metrics are stored and added. Previously, the contraction_metrics and gradnorms_unitcell got a new entry every time cost function was called. These function evaluations don't necessarily match the iterations of the optimization loop. In particular, entries were added for every rejected step during the line search, which just seemed needlessly confusing. I changed to workflow to record the current values in the cost function, but only add entries in the finalize! routine, which is actually called after every iteration (i.e. every accepted step).

This is also in anticipation of adding preconditioning, which naturally requires access to the current gradient norm.

[codecov]

Codecov Report

❌ Patch coverage is 95.12195% with 2 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
src/algorithms/optimization/peps_optimization.jl	95.65%	1 Missing ⚠️
src/algorithms/select_algorithm.jl	94.11%	1 Missing ⚠️

Files with missing lines	Coverage Δ
src/Defaults.jl	85.71% <ø> (ø)
...rithms/optimization/fixed_point_differentiation.jl	85.47% <100.00%> (-3.33%)	⬇️
src/algorithms/optimization/peps_optimization.jl	95.65% <95.65%> (+0.91%)	⬆️
src/algorithms/select_algorithm.jl	82.05% <94.11%> (+5.12%)	⬆️

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