Optimization, Modeling, and Simulation Training
|Commitment||2 days, 7-8 hours a day.|
|How To Pass||Pass all graded assignments to complete the course.|
|User Ratings||Average User Rating 4.8 See what learners said|
|Delivery Options||Instructor-Led Onsite, Online, and Classroom Live|
Optimization, Modeling, and Simulation Training Course – Hands-on
Optimization, Modeling, and Simulation Training Course – Customize it
- We can adapt this training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this training course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the training around the mix of technologies of interest to you (including technologies other than those included in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the training course in manner understandable to lay audiences.
Optimization, Modeling, and Simulation Training Course – Audience/Target Group
The target audience for this training course:
Optimization, Modeling, and Simulation Training Course – Objectives:
Upon completing this training course, learners will be able to meet these objectives:
- Popular methods for stochastic optimization.
- To recognize when stochastic optimization techniques are necessary or beneficial.
- Advantages and disadvantages of popular methods for system optimization.
- Essential theoretical principles and assumptions underlying optimization and Monte Carlo simulation and the implications for practical implementation.
- Basics of mathematical modeling and the link to Monte Carlo simulation.
- State-of-the-art methods for using Monte Carlo simulations to improve real system performance.
Optimization, Modeling, and Simulation Training – Course Content
Brief Mathematical Review. Relevant multivariate analysis, matrix algebra, probability, and statistics.
Background on Search and Optimization. Basic issues and definitions. Stochastic vs. deterministic methods. No free lunch theorems for optimization. Summary of classical methods of optimization and their limitations.
Direct Search Techniques. Introduction to direct random search. Monte Carlo methods. Nonlinear simplex (Nelder-Mead) algorithms.
Least-Squares-Type Methods. Recursive methods for linear systems. Recursive least squares (RLS). Least mean squares (LMS). Connection to Kalman filtering.
Stochastic Approximation for Linear and Nonlinear Systems. Root-finding and gradient-based stochastic approximation (Robbins-Monro). Gradient-free stochastic approximation: finite-difference (FDSA) and simultaneous perturbation (SPSA) methods.
Search Methods Motivated by Physical Processes. Simulated annealing and related methods. Evolutionary computation and genetic algorithms.
Discrete stochastic optimization. Statistical methods (e.g., ranking and selection, multiple comparisons), general random search methods, and discrete simultaneous perturbation SA (DSPSA).
Model Building. Issues particular to Monte Carlo simulation models. Bias-variance tradeoff. Selecting “best” model via cross-validation. Fisher information matrix as summary measure.
Simulation-Based Optimization. Use of Monte Carlo simulations to improve performance of real-world system performance. Gradient-based methods (infinitesimal perturbation analysis and likelihood ratio) and non-gradient-based methods (FDSA, SPSA, etc.). Common random numbers.
Markov Chain Monte Carlo. Monte Carlo methods for difficult calculations; Metropolis-Hastings and Gibbs sampling. Applications to numerical integrat ion and statistical estimation.
Input Selection and Experimental Design. Classical vs. optimal design. A practical criterion for optimal design (D-optimality). Input selection in linear and nonlinear models.