Add Preconditioned Conjugate Gradient Method

[julianlitz]

Add Preconditioned Conjugate Gradient (PCG) to GMGPolar

Rather than running GMGPolar as a standalone iterative solver, we can exploit it as a preconditioner for CG.

Each PCG iteration approximates the solution of A*z=r by running a cheap FMG approximation followed by one multigrid cycle. A fast yet sufficiently accurate approximation.

The non-extrapolated operator A is used as the preconditioner M, both for solving the standard and extrapolated problem (see Litz et al.).
The extrapolated operator A_ex cannot serve as preconditioner because PCG only has access to a discretized residual vector at each iteration instead of a source term function specific to each level. While we could restrict the residual and try to solve the extrapolated system arising from that, it won't converge to a higher order solution. This was validated on the paper_v2_conjugate_gradient branch.

When combined with extrapolation, the PCG solver converges in up to 4× fewer iterations than standalone GMGPolar and runs up to 2× faster end-to-end. A significant gain for a minimal implementation cost.
This is mainly contributed by the fact that the extrapolated smoother with slow algebraic convergence is not used.

The implementation is tightly integrated with the existing solver, so the additional memory cost is just two vectors. The three PCG work vectors (r, z, A*p) are aliased onto storage that already exists (level.rhs(), level.solution(), level.residual()), and only the solution x and search direction p require fresh allocations.

Usage:
bool PCG = true/false toggles between the standard iterative GMGPolar solver and the new PCG approach.

FMG is used just as before to obtain the initial approximation x_0.

There are multiple settings that determine how precise the A*z=r solve will be.
First we compute a cheap approximation via bool PCG_FMG=true
which is then refined with some PCG_MG_iterations.

PCG_FMG=false and PCG_MG_iterations=0 is equal to the standard CG method without preconditioning.

This PR adds the PCG algorithm
https://github.com/SciCompMod/GMGPolar/blob/litz_pcg_01/src/GMGPolar/solver.cpp#L308-#L428
as well as google tests to test the convergence of the new method.

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[codecov]

Codecov Report

❌ Patch coverage is 78.30882% with 59 lines in your changes missing coverage. Please review.
✅ Project coverage is 94.91%. Comparing base (aac9b10) to head (e8d7055).

Files with missing lines	Patch %	Lines
src/GMGPolar/setup.cpp	8.82%	31 Missing ⚠️
src/ConfigParser/config_parser.cpp	52.94%	16 Missing ⚠️
src/GMGPolar/solver.cpp	92.98%	8 Missing ⚠️
src/GMGPolar/gmgpolar.cpp	94.44%	3 Missing ⚠️
src/Level/level.cpp	85.71%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #188      +/-   ##
==========================================
- Coverage   95.28%   94.91%   -0.38%     
==========================================
  Files          94       94              
  Lines        9316     9517     +201     
==========================================
+ Hits         8877     9033     +156     
- Misses        439      484      +45     

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