matlab优化算法 100例

Matlab优化算法是一种基于数学模型的计算方法，在许多领域中都有着广泛的应用。根据实际应用需求，我们可以选择适合的优化算法。下面罗列100个常见的Matlab优化算法：

1. 遗传算法(Genetic Algorithm)
2. 粒子群算法(Particle Swarm Optimization)
3. 差分进化算法(Differential Evolution)
4. 蚁群算法(Ant Colony Optimization)
5. 模拟退火算法(Simulated Annealing)
6. 人工鱼群算法(Artificial Fish Swarm Algorithm)
7. 历史遗传算法(Historical Genetic Algorithm)
8. 协方差矩阵适应进化策略(Convex Matrix Evolution Strategy)
9. 盲化梯度下降(Blind Gradient Descent)
10. 坐标下降法(Coordinate Descent)
11. 简单x方法(Simplex Method)
12. 对偶内点法(Dual Interior Point Method)
13. 增广拉格朗日法(Augmented Lagrangian Method)
14. 卡尔曼滤波(Kalman Filter)
15. 扩展卡尔曼滤波(Extended Kalman Filter)
16. 动态规划(Dynamic Programming)
17. 灰关联分析(Grey Relational Analysis)
18. 纯虚拟炼金术模拟算法(Purely Virtual Alchemy Simulation Algorithm)
19. 模糊控制算法(Fuzzy Control Algorithm)
20. 归纳逻辑程序设计算法(Inductive Logic Programming Algorithm)
21. Linear Programming
22. Nonlinear Programming
23. Quadratic Programming
24. Integer Programming
25. Semi-definite Programming
26. Combinatorial Optimization
27. Stochastic Programming
28. Convex Optimization
29. Non-negative Matrix Factorization
30. Support Vector Machine
31. Logistic Regression
32. Linear Discriminant Analysis
33. Naive Bayes Classifier
34. Principal Component Analysis
35. Independent Component Analysis
36. Karhunen-Loeve Transform
37. Wavelet Transform
38. Discrete Wavelet Transform
39. Fast Fourier Transform
40. Nonlinear Least Squares
41. Maximum Likelihood Estimation
42. Conditional Maximum Likelihood Estimation
43. Maximum A Posteriori Estimation
44. Sequential Monte Carlo
45. Markov Chain Monte Carlo
46. Gibbs Sampling
47. Metropolis-Hastings Algorithm
48. Hamiltonian Monte Carlo
49. Variational Bayes
50. Expectation-Maximization Algorithm
51. Structured Variational Bayes
52. Belief Propagation
53. Compressed Sensing
54. Sparse Representation
55. Non-negative Sparse Representation
56. Robust Principal Component Analysis
57. Low Rank Matrix Completion
58. Nonlinear Regression
59. Kernel Regression
60. Gaussian Process Regression
61. Kriging
62. Smoothing Splines
63. Nonparametric Regression
64. Discriminant Analysis
65. Nonparametric Bayes
66. Boosting
67. Random Forest
68. Deep Learning
69. Convolutional Neural Network
70. Recurrent Neural Network
71. Long Short-Term Memory
72. Autoencoder
73. Variational Autoencoder
74. Generative Adversarial Network
75. Reinforcement Learning
76. Q-Learning
77. Policy Gradient
78. Actor-Critic
79. Monte Carlo Tree Search
80. Exploration-Exploitation Dilemma
81. Batch Reinforcement Learning
82. Decision Trees
83. Naive Decision Trees
84. Randomized Decision Trees
85. Regression Trees
86. Tree Boosting
87. Gradient Boosting
88. Adaboost
89. Rank Boosting
90. Decision Forest
91. Learning to Rank
92. Unsupervised Learning
93. Clustering
94. K-Means
95. Gaussian Mixture Model
96. Hierarchical Clustering
97. Self-Organizing Maps
98. Non-negative Matrix Factorization Clustering
99. Subspace Clustering
100. Latent Dirichlet Allocation.