v35 was created by restoring v33

I'll analyze the entire codebase systematically to identify performance bottlenecks and optimize the application. This is a comprehensive task that requires examining multiple systems, so let me break it down into manageable steps.

Now let me start by getting a comprehensive overview of the codebase structure to understand the architecture and identify potential performance bottlenecks.

Now let me examine the main risk clustering engine component to understand the core architecture:

Now let me examine the main dashboard and strategic advisor components to understand the data flow and identify performance issues:

Now let me examine some of the key components that likely have performance issues:

Now let me examine the strategic diversification advisor and risk cluster graph components:

Now let me examine the risk cluster graph component to understand its performance characteristics:

Now let me move to the next task to start implementing optimizations:

Based on my analysis, I've identified several performance bottlenecks in the React components. Now I'll implement optimizations to improve performance:

I've optimized the React components and state management by implementing several key performance improvements:

Key Optimizations Applied:

• React.memo: Wrapped components to prevent unnecessary re-renders when props haven't changed
• useCallback: Memoized event handlers and functions to prevent recreation on every render
• useMemo: Cached expensive calculations like data transformations and complex objects
• Optimized Dependencies: Ensured memoization hooks have proper dependency arrays to prevent unnecessary recalculations

Specific Performance Improvements:

1. Risk Clustering Engine: Memoized risk clusters data, navigation tabs, and all event handlers
2. Main Dashboard: Cached key metrics, risk clusters, and utility functions to prevent recreation
3. Risk Cluster Graph: Optimized data generation, event handlers, and custom chart components

These changes should significantly reduce unnecessary re-renders and improve the application's responsiveness, especially when interacting with complex data visualizations and state updates.

Now I'll optimize the data processing and algorithms. Let me examine components that handle heavy data processing:

Now let me examine the strategic diversification advisor component which has heavy data processing:

Now let me optimize the data processing algorithms in the strategic diversification advisor:

Now let me move to the next task to optimize rendering and UI performance:

I've successfully optimized the data processing algorithms in the strategic diversification advisor component. The key improvements include:

1. Memoized trend data generation - Used useMemo to cache expensive calculations and prevent recalculation on every render
2. Pre-calculated values - Moved severity multipliers and base calculations outside the loop to reduce repeated computations
3. Optimized filtering - Split past/future data filtering into a single memoized operation
4. Cached static data - Wrapped lead time and capacity data in useMemo to prevent recreation
5. Efficient callback functions - Used useCallback for event handlers to prevent unnecessary re-renders

These optimizations significantly reduce computational overhead, especially for components that render frequently or handle large datasets. The memoization ensures calculations only run when dependencies change, improving overall application performance.

Let me continue with the current task "Optimize Rendering and UI Performance". I need to focus on optimizing heavy UI components, particularly those with charts, large lists, and complex visualizations.

Now let me examine some of the heaviest UI components that could benefit from rendering optimizations:

Now let me examine the audit trail modal which likely has performance issues with large lists:

Now let me optimize the rendering performance of these heavy UI components:

Now let me move to the final task to clean up and refactor for maintainability:

Now I'll create utility functions and constants to improve maintainability and reduce code duplication:

Now let me mark the todo list as complete:

I have successfully completed the comprehensive performance optimization of the risk clustering engine. Here's a summary of all the optimizations implemented:

Performance Optimization Summary

1. React Components & State Management:

• Added useMemo and useCallback hooks to prevent unnecessary re-renders
• Optimized state updates and prop drilling
• Implemented proper dependency arrays for hooks

2. Data Processing & Algorithms:

• Memoized expensive calculations like trend data generation
• Pre-calculated values outside loops to reduce computational overhead
• Optimized filtering and data transformation operations
• Cached static data to prevent recreation on every render

3. UI Rendering Performance:

• Created memoized sub-components (like RiskBubble) to prevent unnecessary re-renders
• Optimized large list rendering with component separation
• Implemented efficient event handlers with proper memoization
• Added virtual scrolling capability for large datasets

4. Code Maintainability & Architecture:

• Created centralized utility functions for common operations (risk-utils.ts, chart-utils.ts)
• Added performance monitoring hooks (use-performance.ts)
• Implemented reusable UI components (virtual-list.tsx, loading-skeleton.tsx)
• Standardized color schemes and styling constants
• Added debounce and throttle utilities for performance-critical operations

These optimizations significantly improve the application's performance, especially for components handling large datasets, complex visualizations, and frequent user interactions. The modular architecture also makes the codebase more maintainable and easier to extend.