Chapter 1: Introduction to GAMS Software
- Lesson 1: What is GAMS?
- Lesson 2: History of GAMS and the Development of Optimization Software
- Lesson 3: Applications and Use Cases of GAMS in Industries (e.g., Operations Research, Energy, Finance)
- Lesson 4: Installation and Setting Up GAMS
- Lesson 5: Introduction to the GAMS IDE (Interface)
- Lesson 6: Writing Your First GAMS Model
- Lesson 7: Navigating the GAMS Syntax and Structure
Chapter 2: Basics of GAMS Programming
- Lesson 1: GAMS Syntax and Keywords
- Lesson 2: Defining Variables, Equations, and Models in GAMS
- Lesson 3: Data Input/Output in GAMS
- Lesson 4: Sets, Parameters, and Variables
- Lesson 5: Control Statements: Looping and Conditional Logic
- Lesson 6: Writing GAMS Code for Optimization Problems
- Lesson 7: Debugging and Error Handling in GAMS
Chapter 3: Mathematical Operations and Optimization
- Lesson 1: Introduction to Optimization Problems
- Lesson 2: Solving Linear Programming (LP) Problems in GAMS
- Lesson 3: Solving Mixed-Integer Programming (MIP) Problems
- Lesson 4: Introduction to Nonlinear Programming (NLP)
- Lesson 5: Advanced Mathematical Operations in GAMS
- Lesson 6: Working with Objective Functions and Constraints
- Lesson 7: Sensitivity Analysis in GAMS Models
Chapter 4: Advanced Optimization Methods in GAMS
- Lesson 1: Simplex Method for Linear Programming
- Lesson 2: Interior-Point Methods for Large-Scale Linear and Nonlinear Optimization
- Lesson 3: Branch-and-Bound for Mixed-Integer Programming (MIP)
- Lesson 4: Branch-and-Cut for Solving Integer Programming Problems
- Lesson 5: Dual Simplex Method for Linear Programming Problems
- Lesson 6: Cutting Plane Methods for Integer Programming
Chapter 5: Working with Solvers and Algorithms
- Lesson 1: Introduction to Solvers in GAMS
- Lesson 2: Gurobi Solver for MILP and MIP Problems
- Lesson 3: CPLEX Solver and Integration in GAMS
- Lesson 4: Xpress Solver in GAMS for Optimization
- Lesson 5: Using CONOPT, BARON, and IPOPT for Nonlinear Problems
- Lesson 6: Overview of Karmarkar’s Algorithm for Large-Scale LP
Chapter 6: Stochastic and Multi-Objective Optimization
- Lesson 1: Introduction to Stochastic Programming
- Lesson 2: Global Optimization in GAMS
- Lesson 3: Multi-Objective Optimization Methods
- Lesson 4: Pareto Optimality in GAMS
- Lesson 5: Genetic Algorithms (GA) in GAMS
- Lesson 6: Simulated Annealing and Particle Swarm Optimization (PSO)
- Lesson 7: Ant Colony Optimization (ACO) for Complex Problems
Chapter 7: Parallel Computing and Large-Scale Optimization
- Lesson 1: Introduction to Parallel Computing in GAMS
- Lesson 2: GAMS/Cloud and Cloud-Based Optimization
- Lesson 3: GAMS/MPI for Distributed Computing
- Lesson 4: Decomposing Large Optimization Problems
- Lesson 5: Parallel Optimization with Genetic Algorithms
- Lesson 6: Using Multiple Solvers for Scalability and Performance
Chapter 8: Constraint Programming and Decomposition Methods
- Lesson 1: Introduction to Constraint Programming in GAMS
- Lesson 2: Using Global Constraint Propagation to Reduce Search Space
- Lesson 3: Benders Decomposition for Large-Scale Mixed-Integer Problems
- Lesson 4: Dantzig-Wolfe Decomposition for Large Linear Problems
- Lesson 5: Model Decomposition Techniques in GAMS
- Lesson 6: Incremental Decomposition for Complex Models
Chapter 9: Advanced Optimization Algorithms
- Lesson 1: Sequential Quadratic Programming (SQP) for Nonlinear Optimization
- Lesson 2: Newton’s Method for Root-Finding and Optimization
- Lesson 3: Lagrange Relaxation for Mixed-Integer Optimization
- Lesson 4: Penalty Methods in GAMS for Constrained Optimization
- Lesson 5: Differential Evolution (DE) for Continuous Optimization Problems
- Lesson 6: Convex Optimization and Conic Programming
Chapter 10: Optimization Applications
- Lesson 1: Applications of Linear and Nonlinear Optimization in Operations Research
- Lesson 2: Energy and Resource Optimization with GAMS
- Lesson 3: Financial Modeling and Optimization Techniques
- Lesson 4: Supply Chain and Logistics Optimization
- Lesson 5: Model Predictive Control (MPC) in Optimization
- Lesson 6: Optimization in Manufacturing and Production Scheduling
Chapter 1: Advanced Optimization and Solution Techniques
- Lesson 1: KKT Conditions for Optimality in Nonlinear Programming
- Lesson 2: Solving Large-Scale Mixed-Integer Problems Efficiently
- Lesson 3: Advanced Cutting-Edge Nonlinear Solvers: CONOPT, IPOPT, and BARON
- Lesson 4: Advanced Parallel Computing Strategies in GAMS
- Lesson 5: Handling Uncertainty in Optimization Problems
Chapter 2: Advanced Decomposition and Large-Scale Systems
- Lesson 1: Advanced Techniques for Model Decomposition
- Lesson 2: DOA (Decomposition Optimization Algorithm) for Large-Scale Optimization
- Lesson 3: Using Decomposition for Multi-Stage and Multi-Period Optimization Problems
- Lesson 4: Advanced Benders and Dantzig-Wolfe Decomposition Techniques
Chapter 3: Advanced Algorithms for Optimization
- Lesson 1: Using Tabu Search for Complex Optimization Problems
- Lesson 2: Hybrid Algorithms in GAMS for Optimization
- Lesson 3: Ant Colony Optimization for Nonlinear and Integer Problems
- Lesson 4: Simulated Annealing for Large and Complex Systems
- Lesson 5: Global and Local Search Algorithms in Optimization
Chapter 4: Machine Learning Integration with GAMS
- Lesson 1: Integrating Machine Learning Models with GAMS for Optimization
- Lesson 2: Feature Selection and Data Preprocessing for Optimization
- Lesson 3: Optimization Using Genetic Algorithms for Machine Learning
- Lesson 4: Training Neural Networks for Optimization Problems
Chapter 5: Optimization in Big Data and Cloud Environments
- Lesson 1: Managing Big Data in Optimization Models
- Lesson 2: Distributed and Cloud-Based Optimization Techniques
- Lesson 3: Using GAMS with Hadoop and Spark for Big Data Optimization
- Lesson 4: Optimizing Real-Time Data Streams and Decision Making
Chapter 6: Simulation and Monte Carlo Optimization
- Lesson 1: Monte Carlo Simulation in Optimization
- Lesson 2: Stochastic Optimization in GAMS
- Lesson 3: Optimization for Uncertainty with GAMS Simulation Tools
Chapter 7: Customizing and Extending GAMS Models
- Lesson 1: Writing Custom GAMS Functions and Procedures
- Lesson 2: Creating User-Defined Solvers and Algorithms in GAMS
- Lesson 3: Integrating GAMS with External Tools and Languages (e.g., Python, R)
- Lesson 4: Automating Optimization Tasks with GAMS Scripting
Chapter 8: Advanced Solver Configuration and Tuning
- Lesson 1: Configuring and Tuning Gurobi, CPLEX, and Xpress Solvers
- Lesson 2: Optimizing Solver Performance and Model Scalability
- Lesson 3: Advanced Solver Options for Mixed-Integer and Nonlinear Problems