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Welcome to the Cornell University Computational Optimization Open Textbook This electronic textbook is a student-contributed open-source text covering a variety of topics on process optimization. If you have any comments or suggestions on this open textbook, please contact Professor Fengqi You.

Linear Programming (LP) Duality Simplex algorithm Computational complexity Network flow problem Interior-point method for LP Optimization with absolute values Matrix game (LP for game theory) NonLinear Programming (NLP) Line search methods Trust-region methods Interior-point method for NLP Conjugate gradient methods Quasi-Newton methods Quadratic programming Sequential quadratic programming Subgradient optimization Mathematical programming with equilibrium constraints Dynamic optimization Geometric programming Nondifferentiable Optimization Evolutionary multimodal optimization Stackelberg leadership model Quadratic constrained quadratic programming Derivative free optimization Deterministic Global Optimization Exponential transformation Logarithmic transformation McCormick envelopes Piecewise linear approximation Spatial branch and bound method Dynamic Programming Markov decision process Bellman equation Eight step procedures Stochastic dynamic programming Traditional Applications Facility location problem Traveling salesman problem Set covering problem Quadratic assignment problem Job shop scheduling Newsvendor problem Unit commitment problem Portfolio optimization

Mixed-Integer Linear Programming (MILP) Mixed-integer cuts Disjunctive inequalities Lagrangean duality Column generation algorithms Heuristic algorithms Branch and cut Local branching Mixed-Integer NonLinear Programming (MINLP) Signomial problems Mixed-integer linear fractional programming (MILFP) Convex generalized disjunctive programming (GDP) Nonconvex generalized disjunctive programming (GDP) Branch and bound (BB) for MINLP Branch and cut for MINLP Generalized Benders decomposition (GBD) Outer-approximation (OA) Extended cutting plane (ECP) Optimization under Uncertainty Stochastic programming Chance-constraint method Fuzzy programming Classical robust optimization Adaptive robust optimization Data driven robust optimization Optimization for Machine Learning and Data Analytics Stochastic gradient descent Momentum AdaGrad RMSProp Adam Frank-Wolfe Sparse Reconstruction with Compressed Sensing Adadelta Adafactor AdamW Adamax FTRL algorithm Lion algorithm LossScaleOptimizer Nadam Black-box Optimization Bayesian optimization Genetic algorithm Simulated annealing Particle swarm optimization Differential evolution Emerging Applications Wing shape optimization Optimization in game theory Quantum computing for optimization

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