Undergraduate Certificate in Engineering Decision Optimization Techniques
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⢠Introduction to Engineering Decision Optimization Techniques – covers fundamental concepts, principles, and methodologies in engineering decision optimization, including optimization models, algorithms, and solution approaches.
⢠Linear Programming – delves into the mathematics, theory, and applications of linear programming, including simplex method, duality theory, and sensitivity analysis.
⢠Integer Programming – explores the techniques and methods for solving integer programming problems, including branch-and-bound, cutting plane, and heuristic methods.
⢠Nonlinear Programming – examines the optimization of nonlinear objective functions and constraints, including unconstrained optimization, constrained optimization, and optimization with equality constraints.
⢠Network Flows – investigates the optimization of network flow problems, including maximum flow, minimum cost flow, and multi-commodity flow problems.
⢠Discrete Optimization – studies the optimization of discrete structures, including graphs, trees, and matroids.
⢠Stochastic Optimization – covers the optimization of decision problems under uncertainty, including stochastic programming, robust optimization, and chance-constrained optimization.
⢠Large-Scale Optimization – explores the optimization of large-scale engineering systems, including decomposition methods, parallel computing, and distributed optimization.
⢠Optimization Software – examines the use of optimization software tools, including optimization modeling languages, solvers, and libraries.
⢠Optimization Applications – applies optimization techniques to real-world engineering problems, including design optimization, operations research, and decision analysis.
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