Certainly, here's the list formatted with numbers:
1. **Tail-Call Optimization**: Explore methods to optimize tail calls in Haskell, discussing how this technique can help avoid stack overflows in recursive functions.
2. **Closure Implementations**: Delve into closure conversion strategies like lambda lifting and evaluate their impact on performance.
3. **Benchmarking and Profiling**: Employ tools like Criterion for benchmarking and use cost center analysis for profiling. Analyze the performance improvements and ensure the optimizations are effective.
4. **Intermediate Representation**: Discuss the use of intermediate representations like the G-machine in Haskell compiler and its role in optimization.
5. **Garbage Collection**: Evaluate Haskell's garbage collection algorithms, like tricolor marking, and suggest improvements for better memory management.
6. **Inline Expansion**: Assess the inline expansion policy in Haskell, considering when aggressive inlining can be beneficial.
7. **Thunk Evaluation**: Explore strategies like lazy evaluation and its impact on Haskell's performance.
8. **Code Generation**: Discuss targeting platforms like LLVM and how it influences the compiler's efficiency.
9. **Optimization Levels**: Debate the trade-offs involved in different optimization levels, like -O2, in Haskell compilers.
10. **Continuous Integration and Delivery**: Integrate tools like Travis CI and Docker to establish robust CI/CD pipelines for Haskell projects.
11. **Static Analysis and Code Quality**: Utilize tools like HLint for static code analysis and discuss best practices for maintaining code quality in Haskell.
