MatLogica | AADC Integration Guides & Tutorials

AADC Integration Guides & Tutorials

Learn How to Use AADC

Step-by-step guides, integration tutorials, and practical examples for implementing MatLogica AADC in your codebase. From basic setup to advanced optimization techniques.

New to AADC?

Start with these essential resources

Step 1
Video: 5-minute Technical Demo

Short video demonstration of MatLogica AADC interface showing a basic example that's available online. Demonstrates the ease of use and immediate performance benefits.

  • User interface demo
  • Basic AAD example
  • Quick start guide
  • Performance showcase
Watch Video →
Step 2
Integration Guide

Detailed guide on how to integrate MatLogica AADC into existing quantitative finance codebases with minimal disruption.

  • Integration patterns
  • Code modification strategies
  • API usage examples
  • Testing approach
View Guide →
Step 3
Guilt-free Live-Risk in the Cloud: a New AAD-powered Approach

Comprehensive guide to implementing live risk calculations in cloud environments using MatLogica AADC with focus on security performance and scalability.

  • Cloud deployment architecture
  • Secure execution environment
  • Scalable risk calculations
  • Cost optimization
View Article →
Step 4
Automated IFT: Calibration Without Manual Derivatives

Presentation on automated application of the Implicit Function Theorem eliminating manual derivative coding for calibration routines.

  • Automated IFT implementation
  • No manual derivatives
  • Calibration efficiency
  • Production deployment
View Presentation →

Practical Implementation Guides

Learn how to integrate AADC into your codebase with step-by-step tutorials and real-world examples

What You'll Learn:

  • Integration steps: How to add AADC to your project
  • Code patterns: Best practices for AAD implementation
  • Common use cases: Greeks, live risk, calibration
  • Advanced techniques: IFT, smoothing, optimization
  • Migration guides: Moving from other AAD solutions
  • Production tips: Deployment and performance tuning

How To: Calculate Accurate Greeks for Autocallables with AAD

Production-ready solution for autocallable Greeks using smoothing + AAD achieving 90% computational cost reduction while maintaining accuracy for the $104B autocallable market.

Presentation Topics

  • 90% computational cost reduction
  • Smoothing + AAD methodology
  • Production-ready C++ code
  • Open-source benchmarks

Conference Details

  • $104B autocallable market
  • Exotic derivatives pricing
  • Real-time Greeks possible
  • Validated convergence
Read Full Paper

How To: Accurate Greeks for Autocallables with AAD and Smoothing

Production-ready methodology for computing accurate Greeks for autocallable structured products using smoothing techniques combined with automatic adjoint differentiation.

Key Results

  • Smoothing methodology
  • Autocallable Greeks
  • 90% cost reduction
  • Production validation

Applications

  • Structured products
  • Exotic derivatives
  • Risk management
  • Accurate sensitivities
Read Paper

How To: Have Guilt-free Live-Risk in the Cloud: a New AAD-powered Approach

Comprehensive guide to implementing live risk calculations in cloud environments using MatLogica AADC with focus on security performance and scalability.

Topics

  • Cloud deployment architecture
  • Secure execution environment
  • Scalable risk calculations
  • Cost optimization

Benefits

  • Real-time risk analytics
  • Cloud-native design
  • Production deployment
  • Security best practices
View Article

How To: Apply Implicit Function Theorem for Live Risk Practice with QuantLib

Practical guide to using the Implicit Function Theorem for live risk calculations with QuantLib integration demonstrating efficient calibration and risk computation.

Topics

  • Implicit differentiation
  • Calibration automation
  • Live risk calculations
  • QuantLib integration

Benefits

  • Model calibration
  • Real-time Greeks
  • Live Risk
  • Performance gains
View Guide View Presentation

How To: Running CUDA Analytics on Both GPU and CPU with AAD

An elegant approach to run existing CUDA analytics on both GPU and CPU with the added benefit of automatic differentiation for computing sensitivities.

Features

  • Dual GPU/CPU execution
  • CUDA code portability
  • Automatic derivatives
  • Performance flexibility

Advantages

  • Hardware flexibility
  • Cost optimization
  • Fallback options
  • AAD benefits
Download PDF

How To: Perform Integration

Detailed guide on how to integrate MatLogica AADC into existing quantitative finance codebases with minimal disruption.

Integration Steps

  • Integration patterns
  • Code modification strategies
  • API usage examples
  • Testing approach

Benefits

  • Minimal code changes
  • Backward compatibility
  • Incremental adoption
  • Production rollout
View Guide

How To: Apply AAD to Levenberg-Marquardt Minimizer

Demonstrates integration of MatLogica AADC with the Levenberg-Marquardt optimization algorithm for efficient calibration.

Features

  • Automatic Jacobian calculation
  • Fast curve fitting
  • Production-ready code
  • Open-source example

Benefits

  • Model calibration
  • Parameter optimization
  • No manual derivatives
  • Proven accuracy
View Demo

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