| modeling_methods = """\ | |
| ## Operations Research | |
| ### Programming Theory | |
| #### Linear Programming | |
| - Linear Programming (LP) | |
| - Integer Programming (IP) | |
| - Mixed Integer Programming (MIP) | |
| - Goal Programming (GP) | |
| - Multi-Objective Programming (MOP) | |
| - Multi-level Programming | |
| - Dynamic Programming (DP) | |
| - Network Optimization Models | |
| - Parametric Linear Programming | |
| #### Nonlinear Programming | |
| - Convex Programming | |
| - Quadratic Programming (QP) | |
| - Nonlinear Programming (NLP) | |
| - Semi-Definite Programming (SDP) | |
| - Set Programming | |
| - Non-Smooth Optimization | |
| - Penalty Methods in Nonlinear Optimization | |
| #### Others | |
| - Fuzzy Optimization | |
| - Stochastic Optimization | |
| - Robust Optimization | |
| - Approximation Algorithms | |
| - Cooperative Game Theory | |
| - Metaheuristic Approaches (Simulated Annealing, Genetic Algorithms, etc.) | |
| ### Graph Theory | |
| #### Path | |
| - Shortest Path Model (S-T, All-Pairs) | |
| - Dijkstra’s Algorithm | |
| - A* Algorithm | |
| - Bellman-Ford Algorithm | |
| - Eulerian Path Problem | |
| - Hamiltonian Cycle Problem | |
| - Traveling Salesman Problem (TSP) | |
| - Vehicle Routing Problem (VRP) | |
| - K-Shortest Path Problem | |
| - Path Planning Algorithms | |
| #### Tree | |
| - Minimum Spanning Tree (MST) | |
| - Prim’s Algorithm | |
| - Kruskal’s Algorithm | |
| - Huffman Tree | |
| - Steiner Tree Problem | |
| - Binary Search Tree (BST) | |
| - AVL Tree | |
| - K-d Tree | |
| - Quad Tree | |
| - B+ Tree | |
| #### Flow | |
| - Max-Flow Min-Cost Max-Flow Problem | |
| - Ford-Fulkerson Algorithm | |
| - Edmonds-Karp Algorithm | |
| - Minimum-Cost Flow Problem | |
| - Multi-Commodity Flow Problem | |
| - Network Reliability Models | |
| #### Others | |
| - Bipartite Matching Model | |
| - Stable Marriage Problem | |
| - Graph Coloring Problem (Greedy Coloring, Backtracking) | |
| - Vertex Cover Problem | |
| - Set Cover Problem | |
| - Clique Problem | |
| - Independent Set Problem | |
| - Algebraic Representation of Graph (Adjacency Matrix, Laplacian Matrix, Incidence Matrix) | |
| - Spectral Graph Theory Models | |
| ### Stochastic Programming Theory | |
| - Stochastic Linear Programming | |
| - Markov Chains and Models | |
| - Markov Decision Process (MDP) | |
| - Queuing Theory (M/M/1, M/G/1, G/G/1 Queues) | |
| - Inventory Theory (Economic Order Quantity, Newsvendor Problem) | |
| - Monte Carlo Simulation | |
| - Reliability Theory | |
| - Decision Trees and Multi-Stage Decision Problems | |
| - Dynamic Stochastic Optimization | |
| ## Optimization Methods | |
| ### Deterministic Algorithms | |
| - Greedy Algorithm | |
| - Divide & Conquer | |
| - Dynamic Programming | |
| - Backtracking Algorithms | |
| - Local Search Algorithms | |
| - Branch and Bound | |
| ### Heuristic Algorithms | |
| - Simulated Annealing (SA) | |
| - Tabu Search | |
| - Genetic Algorithm (GA) | |
| - Particle Swarm Optimization (PSO) | |
| - Ant Colony Optimization (ACO) | |
| - Harmony Search Algorithm | |
| - Differential Evolution | |
| - Memetic Algorithm | |
| - Iterative Deepening Search | |
| ### Iterative Algorithms | |
| - Gradient Descent | |
| - Newton's Method | |
| - Coordinate Descent | |
| - Conjugate Gradient Method | |
| - Broyden–Fletcher–Goldfarb–Shanno (BFGS) Method | |
| - Levenberg-Marquardt Algorithm | |
| - Golden-Section Search | |
| - Nelder-Mead Simplex Algorithm | |
| ### Constrained Optimization | |
| - Linear Programming (LP) Solvers (Simplex Method, Interior-Point Method) | |
| - Quadratic Programming (QP) Solvers | |
| - Feasible Direction Method | |
| - Projected Gradient Method | |
| - Augmented Lagrangian Methods | |
| - Lagrange Multipliers | |
| - Karush-Kuhn-Tucker Conditions | |
| - KKT Conditions in Nonlinear Optimization | |
| - Primal-Dual Methods | |
| ### Solution Techniques | |
| - Branch and Bound Method | |
| - Relaxation Methods | |
| - Penalty Function Methods | |
| - Restriction Method | |
| - Lagrange Relaxation | |
| - Antithesis Optimization | |
| - Subgradient Methods | |
| - Multigrid Methods | |
| --- | |
| ## Machine Learning Topics | |
| ### Classification | |
| - K-Nearest Neighbors (KNN) | |
| - Support Vector Machine (SVM) | |
| - Decision Trees | |
| - Random Forest | |
| - Gradient Boosting Machines (GBM) | |
| - XGBoost, LightGBM, CatBoost | |
| - Logistic Regression | |
| - Naive Bayes | |
| - Linear Discriminant Analysis (LDA) | |
| - Quadratic Discriminant Analysis (QDA) | |
| - Neural Networks (Feedforward, Convolutional, Recurrent) | |
| - Deep Learning (CNN, RNN, LSTM) | |
| ### Clustering | |
| - K-Means Algorithm | |
| - K-Means++ Variant | |
| - Expectation-Maximization (EM) | |
| - Self-Organizing Maps (SOM) | |
| - DBSCAN (Density-Based Spatial Clustering) | |
| - Hierarchical Clustering | |
| - Agglomerative and Divisive Clustering | |
| - Spectral Clustering | |
| - Gaussian Mixture Models (GMM) | |
| - Affinity Propagation | |
| - Birch Clustering | |
| ### Regression | |
| - Linear Regression | |
| - Ridge Regression | |
| - Lasso Regression | |
| - Elastic Net Regression | |
| - Poisson Regression | |
| - Logistic Regression (for binary classification) | |
| - Polynomial Regression | |
| - Generalized Linear Models (GLM) | |
| - Non-Linear Regression | |
| - Locally Weighted Regression (Loess) | |
| ### Dimensionality Reduction | |
| #### Linear | |
| - Principal Component Analysis (PCA) | |
| - Canonical Correlation Analysis (CCA) | |
| - Independent Component Analysis (ICA) | |
| - Singular Value Decomposition (SVD) | |
| #### Non-Linear | |
| - Local Linear Embedding (LLE) | |
| - Laplacian Eigenmaps | |
| - t-Distributed Stochastic Neighbor Embedding (t-SNE) | |
| - Isomap | |
| - Autoencoders | |
| ### Ensemble Learning Algorithms | |
| - Bagging Algorithm | |
| - Boosting Algorithm | |
| - Random Forest | |
| - AdaBoost | |
| - Gradient Boosting | |
| - Stacking | |
| - Voting Classifier | |
| - Bootstrap Aggregating | |
| ## Prediction Topics | |
| ### Discrete Prediction | |
| - Markov Decision Process (MDP) | |
| - Hidden Markov Models (HMM) | |
| - Grey Forecasting | |
| - Bayesian Networks | |
| - Difference Equations | |
| - Kalman Filtering | |
| - Particle Filtering | |
| ### Continuous Prediction | |
| #### Time Series Models | |
| - Autoregressive Integrated Moving Average (ARIMA) | |
| - Generalized Autoregressive Conditional Heteroskedasticity (GARCH) | |
| - Exponential Smoothing (Holt-Winters) | |
| - Seasonal Decomposition of Time Series (STL) | |
| - Prophet Model | |
| #### Differential Equation Models | |
| - Ordinary Differential Equations (ODE) | |
| - Stochastic Differential Equations (SDE) | |
| - Infectious Disease Models (SIR, SEIR) | |
| - Population Growth Models | |
| - Lotka-Volterra Models | |
| - Heat Conduction Models | |
| - Predator-Prey Models | |
| - Diffusion Models (e.g., River Pollutant Diffusion) | |
| - Economic Growth Models | |
| - Battle Models (e.g., Lotka-Volterra Models) | |
| ## Evaluation Topics | |
| ### Scoring Evaluation | |
| - Fuzzy Comprehensive Evaluation | |
| - Grey Evaluation Method | |
| - Analytic Hierarchy Process (AHP) | |
| - Analytic Network Process (ANP) | |
| - Data Envelopment Analysis (DEA) | |
| - Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) | |
| - Entropy Weight Method | |
| - Information Entropy Method | |
| - Weighted Sum Method | |
| - Weighted Product Method | |
| - Multi-Criteria Decision Analysis (MCDA) | |
| - PROMETHEE and GAIA | |
| ### Statistical Evaluation | |
| #### Correlation Test | |
| - Pearson Correlation Coefficient | |
| - Spearman's Rank Correlation Coefficient | |
| - Kendall’s Tau Coefficient | |
| - Wilcoxon's Signed Rank Test | |
| - Kruskal-Wallis Test | |
| - Mann-Whitney U Test | |
| #### Goodness of Fit Test | |
| - Analysis of Variance (ANOVA) | |
| - Chi-Square Goodness-of-Fit Test | |
| - Kolmogorov-Smirnov Test (KS Test) | |
| - Anderson-Darling Test | |
| - Shapiro-Wilk Test | |
| - Jarque-Bera Test | |
| - Bayesian Information Criterion (BIC) | |
| """ |