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preface i optimization models 1 0ne variable optimization 1.1 the five-step method 1.2 sensitivity analysis 1.3 sensitivity and robustness 1.4 exercises 2 multivariable optimization 2.1 unconstrained optimization 2.2 lagrange multipliers 2.3 sensitivity analysis and shadow prices 3 computational methods for optimization 3.1 0ne variable optimization 3.2 multivariable optimization 3.3 linear programming 3.4 discrete optimization 3.5 exercises ii dynamic model 4 introduction to dynamic models .4.1 steady state analysis 4.2 dynamical systems 4.3 discrete time dynamical systems 4.4 exercises 5 analysis of dynamic models 5.1 eigenvalue methods 5.2 eigenvalue methods for discrete system 5.3 phrase portrait 5.4 exercises 6 simulation of dynamic models 6.1 introduction to simulation 6.2 continuous time models 6.3 the euler method 6.4 chaos and fractals 6.5 exercises iii probability models 7 introduction to probability models 7.1 discrete probability models 7.2 continuous probability models 7.3 introduction to statistics 7.4 diffusion 7.5 exercises 8 stochastic models 8.1 markov chains 8.2 markov processes 8.3 linear regression 8.4 time series 8.5 exercises 9 simulation of probability models 9.1 monte carlo simulation 9.2 the markov property 9.3 analytic simulation 9.4 exercises afterword index |
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