Optimize SA inner loop for 2-3.5x throughput improvement

[RishiDesai]

Cache-friendly data structures and fused neighbor loops that preserve bit-identical output for any given seed.

Changes:

1. Flat contiguous adjacency matrix (Matrix.hpp)

   • Replace vector<vector> with single T* array
   • Eliminates pointer chase per adjMatrix.get()
   • Biggest win on large networks (170MB matrix → 3.5x speedup)

2. CSR (Compressed Sparse Row) neighbor lists (Graph.hpp/cpp)

   • Pack all neighbor lists into one contiguous allocation
   • adjSpan(node) returns lightweight span over CSR data

3. Fused neighbor-loop for EC+ED+ER+EGM+EMIN (SANA.cpp)

   • Single loop iterates neighbors once, accumulates all deltas
   • EC-only fast path + full fused path for weighted measures
   • Applied to both performChange and performSwap

4. Cached measure pointers (SANA.hpp/cpp)

   • Eliminates per-iteration string-keyed map lookups

5. WeightedMeasure::getEdgeScore made public

6. vector → vector<uint8_t> for assignedNodesG2

   • Direct byte-addressable access in tight loops

7. Compiler flags: -DNDEBUG -flto (Makefile)

   • Remove assert() calls, enable link-time optimization

8. attribute((flatten)) on performChange/performSwap

9. CSR in inducedEdgesIncChangeOp

Note:
I implemented these optimizations for long-horizon LLM eval tasks. But these changes can be folded in easily for a serious speed and memory boost. Much of my PR is vibe coded, so merge with caution.

Yes - I realize this PR does not follow many of the style guidelines from the contributions file. But I think the speedup strategies above are super useful, even for reference.