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AD: Current Capabilities. Fortran 77: ADIFOR 2.0/3.0Robust, mature tool with excellent language coverageExcellent compiler analysisEfficient forward mode (small number of independents)Adequate reverse mode (small number of dependents)C/C : ADIC 2.0Semi-mature tool with full C language coverag
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1. Automatic Differentiation: Introduction Automatic differentiation (AD) is a technology for transforming a subprogram that computes some function into a subprogram that computes the derivatives of that function
Derivatives used in optimization, nonlinear solvers, sensitivity analysis, uncertainty quantification
Forward mode of AD is efficient for problems with few independent variables or Jacobian-vector products
Reverse mode of AD is efficient for problems with few dependent variables or JTv products
Efficiency of generated code depends on sophistication of underlying compiler analysis and combinatorial algorithms
2. AD: Current Capabilities Fortran 77: ADIFOR 2.0/3.0
Robust, mature tool with excellent language coverage
Excellent compiler analysis
Efficient forward mode (small number of independents)
Adequate reverse mode (small number of dependents)
C/C++: ADIC 2.0
Semi-mature tool with full C language coverage
Sophisticated differentiation algorithms
Efficient forward mode
Fortran 90: OpenAD/F
New tool with partial language coverage
Sophisticated differentiation algorithms
Accurate and novel compiler analysis
Innovative templating mechanism
Efficient forward and reverse modes
3. AD: Application Highlight