Add comprehensive tests for dual preference system

This commit adds extensive tests to ensure the dual preference system works
correctly in LinearSolveAutotune. The tests verify that both best_algorithm_*
and best_always_loaded_* preferences are always set properly.

## New Test Coverage

### Algorithm Classification Tests
- Tests is_always_loaded_algorithm() function for accuracy
- Verifies always-loaded algorithms: LU, Generic, MKL, AppleAccelerate, Simple
- Verifies extension-dependent algorithms: RFLU, FastLU, BLIS, GPU algorithms
- Tests unknown algorithm handling

### Best Always-Loaded Algorithm Finding Tests
- Tests find_best_always_loaded_algorithm() with mock benchmark data
- Verifies data-driven selection from actual performance results
- Tests handling of missing data and unknown element types
- Confirms correct performance-based ranking

### Dual Preference System Tests
- Tests complete dual preference setting workflow with benchmark data
- Verifies both best_algorithm_* and best_always_loaded_* preferences are set
- Tests preference retrieval in new structured format
- Confirms actual LinearSolve preference storage
- Tests preference clearing for both types

### Fallback Logic Tests
- Tests fallback logic when no benchmark data available
- Verifies intelligent heuristics for real vs complex types
- Tests conservative fallback for complex types (avoiding RFLU issues)
- Confirms fallback selection based on element type characteristics

### Integration Tests
- Tests that autotune_setup() actually sets dual preferences
- Verifies end-to-end workflow from benchmarking to preference setting
- Tests that always_loaded algorithms are correctly classified
- Confirms preference validation and type safety

## Test Quality Features
- Mock data with realistic performance hierarchies
- Comprehensive edge case coverage (missing data, unknown types)
- Direct verification of LinearSolve preference storage
- Clean test isolation with proper setup/teardown

These tests ensure that the dual preference system is robust and always
sets both preference types correctly, providing confidence in the
fallback mechanism for production deployments.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

Author: ChrisRackauckas