MatrixOptim
About
I Algorithms
1
Linear Programming (LP)
1.1
Simplex Method
1.2
Duality Theory
1.3
Sensitivity Analysis
2
Mixed Integer Linear Programming
3
Decomposition Algorithms
3.1
Benders for Standard MILP
3.2
L-Benders for Sto-Prog
3.3
L-Benders with Second Stage Int-Var
4
Network Optimization
4.1
Minimum Cost Flow (MCF)
5
Non-Linear Programming (NLP)
5.1
Classical Methods of Calculus
5.2
Separable Programming
5.3
KKT Conditions
5.4
Convex Optimisation
6
Decentralized Optimization
7
Approximation via Relaxation
7.1
of Integer Programming
7.2
Lagrangian Relaxation
8
Metaheuristics
8.1
Bayesian Optimization
9
Dynamic Programming
9.1
Free DynProg
9.2
Deterministic DynProg
II Operations Research
10
Decision Making under Uncertainty
10.1
Bayesian Decision Analysis
10.2
Stochastic Programming
10.3
Robust Programming
10.4
Newsvendor Problem
11
Game Theory
11.1
Mechanism Design
12
Markov Decision Process (MDP)
12.1
Markov Chain
12.2
Queueing Theory
12.3
Inventory Management
III Application
13
Double Auction
13.1
Periodic DA (PDA)
13.2
Continuous DA (CDA)
14
Power System Balancing (PSB)
14.1
Unit Commitment (UC)
14.2
Construction & UC (UCC)
14.3
Optimal Power Flow (OPF)
15
Model Predictive Control (MPC)
16
Revenue Management (RM)
16.1
Dynamic Pricing
16.2
Scalper
16.3
with Strategic Customers
17
Multi-Agent System (MAS)
17.1
Cooperative MAS
17.2
Noncooperative MAS
18
Intermediation
18.1
Virtual Power Plant (VPP)
18.2
Dynamic Procurement & Pricing (DPP)
18.3
Principal-Agent Problem (PA)
References
Appendix
A
Linear Algebra
Published with bookdown
MatrixOptim
8.1
Bayesian Optimization
GitHub/fmfn/BayesianOptimization