Objective Max Abs Slope of Function Active Objective Function Gradient Step Search Iter Restarts Calls Constraints Function Change Element Size Direction 1 0 2 0 -5.96574 2.29E-6 2.294E-6 1.000 -458E-8 Optimization Results Iterations 1 Function Calls 3 Hessian Calls 2 Active Constraints 0 Objective Function -5.965735903 Max Abs Gradient Element 2.2942694E-6 Slope of Search Direction -4.580223E-6 Ridge 0 ABSGCONV convergence criterion satisfied. The SAS System PROC NLP: Nonlinear Maximization Optimization Results Parameter Estimates Gradient Approx Approx Objective N Parameter Estimate Std Err t Value Pr > |t| Function 1 mean 4.000000 0.894427 4.472138 0.006566 0.000000113 2 sigma 1.999999 0.632455 3.162280 0.025031 0.000002294 Value of Objective Function = -5.965735903 Hessian Matrix mean sigma mean -1.250001147 -1.125884E-7 sigma -1.125884E-7 -2.500005736 Determinant = 3.1250100374 Matrix has Only Negative Eigenvalues Covariance Matrix 2: H = (NOBS/d) inv(G) mean sigma mean 0.7999992658 -3.602817E-8 sigma -3.602817E-8 0.3999990823 Factor sigm = 1 Determinant = 0.3199989722 Matrix has 2 Positive Eigenvalue(s) Approximate Correlation Matrix of Parameter Estimates mean mean 1 -6.368951E-8 sigma -6.368951E-8 1 Determinant = 1 Matrix has 2 Positive Eigenvalue(s)
Objective Max Abs Slope of Function Active Objective Function Gradient Step Search Iter Restarts Calls Constraints Function Change Element Size Direction 1 0 2 0 -5.96574 2.29E-6 2.294E-6 1.000 -458E-8 Optimization Results Iterations 1 Function Calls 3 Hessian Calls 2 Active Constraints 0 Objective Function -5.965735903 Max Abs Gradient Element 2.2942694E-6 Slope of Search Direction -4.580223E-6 Ridge 0 ABSGCONV convergence criterion satisfied. The SAS System PROC NLP: Nonlinear Maximization Optimization Results Parameter Estimates Gradient Approx Approx Objective N Parameter Estimate Std Err t Value Pr > |t| Function 1 mean 4.000000 0.894427 4.472138 0.006566 0.000000113 2 sigma 1.999999 0.632455 3.162280 0.025031 0.000002294 Value of Objective Function = -5.965735903 Hessian Matrix mean sigma mean -1.250001147 -1.125884E-7 sigma -1.125884E-7 -2.500005736 Determinant = 3.1250100374 Matrix has Only Negative Eigenvalues Covariance Matrix 2: H = (NOBS/d) inv(G) mean sigma mean 0.7999992658 -3.602817E-8 sigma -3.602817E-8 0.3999990823 Factor sigm = 1 Determinant = 0.3199989722 Matrix has 2 Positive Eigenvalue(s) Approximate Correlation Matrix of Parameter Estimates mean mean 1 -6.368951E-8 sigma -6.368951E-8 1 Determinant = 1 Matrix has 2 Positive Eigenvalue(s)