C

• ► Combinatorial optimization‎ (5 C, 51 P)
• ► Convex analysis‎ (1 C, 48 P)
• ► Convex optimization‎ (1 C, 17 P)

D

• ► Decomposition methods‎ (6 P)

G

• ► Generalized convexity‎ (1 C, 5 P)

L

• ► Linear programming‎ (1 C, 22 P)

M

• ► Mathematical optimization software‎ (51 P)

O

• ► Optimal control‎ (1 C, 25 P)
• ► Optimization algorithms and methods‎ (5 C, 115 P)
• ► Optimization in vector spaces‎ (2 C, 6 P)
• ► Optimization of ordered sets‎ (1 C, 3 P)

P

• ► Pseudoconvex minimization‎ (1 C)

R

• ► Regression analysis‎ (9 C, 135 P)
• ► Relaxation (approximation)‎ (1 C, 3 P)

S

• ► Stochastic optimization‎ (1 C, 14 P)

V

• ► Variational analysis‎ (5 C, 10 P)

• Calculus of variations
• Energy minimization
• Mathematical optimization
• Optimal design
• Response surface methodology

2

• 2-opt

3

• 3-opt

A

• Active set
• Adaptive simulated annealing
• AIMMS
• Algebraic modeling language
• AMPL
• Analytica (software)
• APMonitor

B

• Backtracking line search
• Backward induction
• Barrier function
• Basis pursuit
• Basis pursuit denoising
• Bayesian efficiency
• Bellman equation
• Benders' decomposition
• Bilevel program
• Bilinear program
• Binary constraint
• Binding constraint
• Bregman method

C

• Cake number
• Candidate solution
• Central composite design
• Chebyshev center
• Combinatorial data analysis
• Complementarity theory
• Compressed sensing
• Conic optimization
• Constraint (mathematics)
• Constraint optimization
• Continuous optimization
• Convex analysis
• Convex optimization
• Corner solution

D

• Danskin's theorem
• Dantzig–Wolfe decomposition
• Database tuning
• Dead-end elimination
• Demand optimization
• Descent direction
• Differential evolution
• Discrete optimization
• Distributed constraint optimization
• DNSS point
• Dual cone and polar cone
• Duality (optimization)
• Duality gap
• Dynamic programming

E

• Entropy maximization
• Extended newsvendor model

F

• Farkas' lemma
• Fenchel's duality theorem
• Fractional programming
• Fritz John conditions

G

• Genetic algorithm
• Genetic programming
• Geometric median
• Geometric programming

G cont.

• Global optimization
• Global optimum
• Goal programming
• Guess value

H

• Hardness of approximation
• Highly optimized tolerance
• Hilbert basis (linear programming)
• Himmelblau's function
• Homicidal chauffeur problem
• Hyper-heuristic

I

• In-crowd algorithm
• Infinite-dimensional optimization
• Inventory control problem
• Iterated conditional modes

J

• Jeep problem
• Job shop scheduling
• Job-shop problem

K

• Karush–Kuhn–Tucker conditions
• Klee–Minty cube

L

• Lagrange multiplier
• Lagrange multipliers on Banach spaces
• Lagrangian relaxation
• Lazy caterer's sequence
• Least absolute deviations
• Least squares
• Lemke's algorithm
• Level set method
• Line search
• Linear complementarity problem
• Linear matrix inequality
• Linear programming decoding
• Linear search problem
• Linear-fractional programming
• Lloyd's algorithm
• Local optimum
• Low-rank approximation

M

• Mathematical Programming Society
• Mathematical programming with equilibrium constraints
• Mathematics of Operations Research
• Matheuristics
• Maxima and minima
• Maximum theorem
• MCACEA
• Meta-optimization
• Metaheuristic
• Mixed complementarity problem
• Mixed linear complementarity problem
• Moving least squares
• MPS (format)
• Multi-objective optimization
• Multidisciplinary design optimization
• Multiprocessor scheduling

N

• Nearest neighbor search
• Newsvendor model
• Nl (format)
• No free lunch in search and optimization
• Non-binding constraint
• Non-linear least squares
• Nonlinear complementarity problem
• Nonlinear programming
• NP-complete

O

• Odds algorithm
• Open shop scheduling
• Operations research
• Optimal control

O cont.

• Optimal stopping
• Optimal substructure
• Ordinal optimization

P

• P versus NP problem
• Paper bag problem
• Paradiseo
• Parallel metaheuristic
• Pareto efficiency
• Pattern search (optimization)
• Perturbation function
• Pontryagin's minimum principle
• Posynomial
• Probabilistic-based design optimization
• Process optimization
• Pseudo-Boolean function
• Pseudoconvex function

Q

• Quadratic programming
• Quadratically constrained quadratic program
• Quasiconvex function

R

• Random optimization
• Rastrigin function
• Reduced cost
• Relaxation (approximation)
• Robbins' problem
• Robust optimization
• Rosenbrock function
• RTCP hierarchical aggregation

S

• Second-order cone programming
• Self-concordant function
• Semi-continuity
• Semidefinite programming
• Shape optimization
• Shekel function
• Signomial
• Single-machine scheduling
• Sion's minimax theorem
• Slack variable
• Slater's condition
• Smoothed analysis
• Sol (format)
• Special ordered set
• Starmad
• Steiner's problem
• Stigler diet
• Stochastic programming
• Stress majorization
• Strong duality
• Subderivative
• Subgradient method
• Successive linear programming
• Sum-of-squares optimization

T

• Ternary search
• Topological derivative
• Topology optimization
• Trajectory optimization
• Trust region

U

• UniSoma

V

• Variational Monte Carlo

W

• Wald's maximin model
• Walrasian auction
• Weak duality
• Wing-shape optimization
• Wolfe conditions