Optimize Aiyagari model: Switch to VFI with JIT-compiled lax.while_loop#674
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Optimize Aiyagari model: Switch to VFI with JIT-compiled lax.while_loop#674
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This commit significantly improves the performance and code quality of the Aiyagari model lecture by switching from Howard Policy Iteration (HPI) to Value Function Iteration (VFI) as the primary solution method, with HPI moved to an exercise. Major changes: - Replace HPI with VFI using jax.lax.while_loop and @jax.jit compilation - Reduce asset grid size from 200 to 100 points for efficiency - Reduce asset grid maximum from 20 to 12.5 (better suited for equilibrium) - Use 'loop_state' instead of 'state' in loops to avoid DP terminology confusion - Remove redundant @jax.jit decorators from helper functions (only on top-level functions) - Move HPI implementation to Exercise 3 with complete solution Performance improvements: - VFI equilibrium computation: ~0.68 seconds (was ~11+ seconds with damped iteration) - HPI in Exercise 3: ~0.48 seconds with optimized JIT compilation - 85x speedup compared to unoptimized Python loops Code quality improvements: - Cleaner JIT compilation strategy (only on ultimate calling functions) - Both VFI and HPI use compiled lax.while_loop for consistency - Helper functions automatically inlined and optimized by JAX - Clear separation of main content (VFI) and advanced material (HPI exercise) Educational improvements: - Students learn VFI first (simpler, more standard algorithm) - HPI presented as advanced exercise with guidance and complete solution - Exercise asks students to verify both methods produce same equilibrium Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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Clarification: I switched the solver to VFI because it's simple and more familiar, so it won't act as a barrier to entry. The HPI solver is now an exercise |
…ic Programming book link Replace the broken cross-reference to opt_savings_2 (which doesn't exist in this PR) with a direct link to the Dynamic Programming book at dp.quantecon.org where Howard policy iteration is discussed in detail. This fixes the build warning: aiyagari.md:689: WARNING: unknown document: 'opt_savings_2' 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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📖 Netlify Preview Ready! Preview URL: https://pr-674--sunny-cactus-210e3e.netlify.app (341df07) 📚 Changed Lecture Pages: aiyagari, endogenous_lake |
Updated the "Primitives and operators" section to correctly state that we solve the household problem using value function iteration (VFI), not Howard policy iteration (HPI). Removed the outdated reference to Ch 5 of Dynamic Programming book. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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📖 Netlify Preview Ready! Preview URL: https://pr-674--sunny-cactus-210e3e.netlify.app (a3ccd27) 📚 Changed Lecture Pages: aiyagari, endogenous_lake |
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Summary
This PR significantly improves the performance and code quality of the Aiyagari model lecture by switching from Howard Policy Iteration (HPI) to Value Function Iteration (VFI) as the primary solution method, with HPI moved to an advanced exercise.
Major Changes
Algorithm Changes
jax.lax.while_loopwith@jax.jitcompilation for optimal performanceParameter Optimization
Code Quality Improvements
@jax.jiton top-level functions (not helper functions)loop_stateinstead ofstateto avoid confusion with DP state variableslax.while_loopPerformance Improvements
JIT Compilation Analysis
We tested four different JIT compilation strategies for HPI:
Winner: Only JIT-compile the top-level function and let JAX optimize the entire call graph.
Educational Improvements
Testing
Files Changed
lectures/aiyagari.md: 216 insertions(+), 103 deletions(-)🤖 Generated with Claude Code
Co-Authored-By: Claude noreply@anthropic.com