In mathematics, computer science and operations research, mathematical optimization or mathematical programming, alternatively spelled optimisation, is the selection of a best element (with regard to some criterion) from some set of available alternatives.[" The Nature of Mathematical Programming ," Mathematical Programming Glossary, INFORMS Computing Society.]
In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics. More generally, optimization includes finding "best available" values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.
Optimization problems
An optimization problem can be represented in the following way:
 Given: a function $f\backslash colon\; A\backslash to\backslash mathbb\{R\}$ from some set $A$ to the
 Sought: an element $\backslash mathbf\{x\}\_\{0\}\backslash in\; A$ such that $f\backslash left(\backslash mathbf\{x\}\_\{0\}\backslash right)\backslash leq\; f\backslash left(\backslash mathbf\{x\}\backslash right)$ for all $\backslash mathbf\{x\}\backslash in\; A$ ("minimization") or such that $f\backslash left(\backslash mathbf\{x\}\_\{0\}\backslash right)\backslash geq\; f\backslash left(\backslash mathbf\{x\}\backslash right)$ for all $\backslash mathbf\{x\}\backslash in\; A$ ("maximization").
Such a formulation is called an optimization problem or a mathematical programming problem (a term not directly related to computer programming, but still in use for example in linear programming – see History below). Many realworld and theoretical problems may be modeled in this general framework. Problems formulated using this technique in the fields of physics and computer vision may refer to the technique as energy minimization, speaking of the value of the function $f$ as representing the energy of the system being modeled.
Typically, $A$ is some subset of the Euclidean space $\backslash mathbb\{R\}^\{n\}$, often specified by a set of constraints, equalities or inequalities that the members of $A$ have to satisfy. The domain $A$ of $f$ is called the search space or the choice set,
while the elements of $A$ are called candidate solutions or feasible solutions.
The function $f$ is called, variously, an objective function, a loss function or cost function (minimization),[W. Erwin Diewert (2008). "cost functions," The New Palgrave Dictionary of Economics, 2nd Edition Contents.] a utility function or fitness function (maximization), or, in certain fields, an energy function or energy functional. A feasible solution that minimizes (or maximizes, if that is the goal) the objective function is called an optimal solution.
In mathematics, conventional optimization problems are usually stated in terms of minimization.
A local minimum
$\backslash mathbf\{x\}^\{\backslash ast\}$
is defined as an element for which there exists some $\backslash delta\; >\; 0$ such that
 for all $\backslash mathbf\{x\}\backslash in\; A$ where $\backslash left\backslash Vert\backslash mathbf\{x\}\backslash mathbf\{x\}^\{\backslash ast\}\backslash right\backslash Vert\backslash leq\backslash delta,\backslash ,$ the expression $f\backslash left(\backslash mathbf\{x\}^\{\backslash ast\}\backslash right)\backslash leq\; f\backslash left(\backslash mathbf\{x\}\backslash right)$ holds;
that is to say, on some region around
$\backslash mathbf\{x\}^\{\backslash ast\}$
all of the function values are greater than or equal to the value at that element.
Local maxima are defined similarly.
While a local minimum is at least as good as any nearby elements, a global minimum is at least as good as every feasible element.
Generally, unless both the objective function and the feasible region are Convex function in a minimization problem, there may be several local minima.
In a convex problem, if there is a local minimum that is interior (not on the edge of the set of feasible elements), it is also the global minimum, but a nonconvex problem may have more than one local minimum not all of which need be global minima.
A large number of algorithms proposed for solving nonconvex problems – including the majority of commercially available solvers – are not capable of making a distinction between locally optimal solutions and globally optimal solutions, and will treat the former as actual solutions to the original problem. Global optimization is the branch of applied mathematics and numerical analysis that is concerned with the development of deterministic algorithms that are capable of guaranteeing convergence in finite time to the actual optimal solution of a nonconvex problem.
Notation
Optimization problems are often expressed with special notation. Here are some examples:
Minimum and maximum value of a function
Consider the following notation:
 $\backslash min\_\{x\backslash in\backslash mathbb\; R\}\backslash ;\; (x^2\; +\; 1)$
This denotes the minimum value of the objective function $x^2\; +\; 1$, when choosing x from the set of $\backslash mathbb\; R$. The minimum value in this case is $1$, occurring at $x\; =\; 0$.
Similarly, the notation
 $\backslash max\_\{x\backslash in\backslash mathbb\; R\}\backslash ;\; 2x$
asks for the maximum value of the objective function 2 x, where x may be any real number. In this case, there is no such maximum as the objective function is unbounded, so the answer is "infinity" or "undefined".
Optimal input arguments
Consider the following notation:
 $\backslash underset\{x\backslash in(\backslash infty,1]\}\{\backslash operatorname\{arg\backslash ,min\}\}\; \backslash ;\; x^2\; +\; 1,$
or equivalently
 $\backslash underset\{x\}\{\backslash operatorname\{arg\backslash ,min\}\}\; \backslash ;\; x^2\; +\; 1,\; \backslash ;\; \backslash text\{subject\; to:\}\; \backslash ;\; x\backslash in(\backslash infty,1].$
This represents the value (or values) of the argument x in the interval $(\backslash infty,1]$ that minimizes (or minimize) the objective function x^{2} + 1 (the actual minimum value of that function is not what the problem asks for). In this case, the answer is x = 1, since x = 0 is infeasible, i.e. does not belong to the feasible set.
Similarly,
 $\backslash underset\{x\backslash in5,5,\; \backslash ;\; y\backslash in\backslash mathbb\; R\}\{\backslash operatorname\{arg\backslash ,max\}\}\; \backslash ;\; x\backslash cos(y),$
or equivalently
 $\backslash underset\{x,\; \backslash ;\; y\}\{\backslash operatorname\{arg\backslash ,max\}\}\; \backslash ;\; x\backslash cos(y),\; \backslash ;\; \backslash text\{subject\; to:\}\; \backslash ;\; x\backslash in5,5,\; \backslash ;\; y\backslash in\backslash mathbb\; R,$
represents the $(x,y)$ pair (or pairs) that maximizes (or maximize) the value of the objective function $x\backslash cos(y)$, with the added constraint that x lie in the interval $5,5$ (again, the actual maximum value of the expression does not matter). In this case, the solutions are the pairs of the form (5, 2kπ) and (−5,(2k+1)π), where k ranges over all .
arg min and arg max are sometimes also written argmin and argmax, and stand for argument of the minimum and argument of the maximum.
History
Fermat and Lagrange found calculusbased formulae for identifying optima, while
Isaac Newton and Gauss proposed iterative methods for moving towards an optimum.
The term "linear programming" for certain optimization cases was due to George Dantzig, although much of the theory had been introduced by Leonid Kantorovich in 1939. ( Programming in this context does not refer to computer programming, but from the use of program by the United States military to refer to proposed training and logistics schedules, which were the problems Dantzig studied at that time.) Dantzig published the Simplex algorithm in 1947, and John von Neumann developed the theory of duality in the same year.
Other major researchers in mathematical optimization include the following:
Major subfields

Convex programming studies the case when the objective function is convex function (minimization) or Concave function (maximization) and the constraint set is convex set. This can be viewed as a particular case of nonlinear programming or as generalization of linear or convex quadratic programming.

Linear programming (LP), a type of convex programming, studies the case in which the objective function f is linear and the constraints are specified using only linear equalities and inequalities. Such a constraint set is called a polyhedron or a polytope if it is Bounded set.

Second order cone programming (SOCP) is a convex program, and includes certain types of quadratic programs.

Semidefinite programming (SDP) is a subfield of convex optimization where the underlying variables are semidefinite matrices. It is a generalization of linear and convex quadratic programming.

Conic programming is a general form of convex programming. LP, SOCP and SDP can all be viewed as conic programs with the appropriate type of cone.

Geometric programming is a technique whereby objective and inequality constraints expressed as posynomials and equality constraints as monomials can be transformed into a convex program.

Integer programming studies linear programs in which some or all variables are constrained to take on integer values. This is not convex, and in general much more difficult than regular linear programming.

Quadratic programming allows the objective function to have quadratic terms, while the feasible set must be specified with linear equalities and inequalities. For specific forms of the quadratic term, this is a type of convex programming.

Fractional programming studies optimization of ratios of two nonlinear functions. The special class of concave fractional programs can be transformed to a convex optimization problem.

Nonlinear programming studies the general case in which the objective function or the constraints or both contain nonlinear parts. This may or may not be a convex program. In general, whether the program is convex affects the difficulty of solving it.

Stochastic programming studies the case in which some of the constraints or parameters depend on .

Robust programming is, like stochastic programming, an attempt to capture uncertainty in the data underlying the optimization problem. Robust optimization targets to find solutions that are valid under all possible realizations of the uncertainties.

Combinatorial optimization is concerned with problems where the set of feasible solutions is discrete or can be reduced to a discrete one.

Stochastic optimization is used with random (noisy) function measurements or random inputs in the search process.

Infinitedimensional optimization studies the case when the set of feasible solutions is a subset of an infinite space, such as a space of functions.

Heuristics and make few or no assumptions about the problem being optimized. Usually, heuristics do not guarantee that any optimal solution need be found. On the other hand, heuristics are used to find approximate solutions for many complicated optimization problems.

Constraint satisfaction studies the case in which the objective function f is constant (this is used in artificial intelligence, particularly in automated reasoning).

Constraint programming is a programming paradigm wherein relations between variables are stated in the form of constraints.

Disjunctive programming is used where at least one constraint must be satisfied but not all. It is of particular use in scheduling.

Space mapping is a concept for modeling and optimization of an engineering system to highfidelity (fine) model accuracy exploiting a suitable physically meaningful coarse or surrogate model.
In a number of subfields, the techniques are designed primarily for optimization in dynamic contexts (that is, decision making over time):

Calculus of variations seeks to optimize an action integral over some space to an extremum by varying a function of the coordinates.

Optimal control theory is a generalization of the calculus of variations which introduces control policies.

Dynamic programming studies the case in which the optimization strategy is based on splitting the problem into smaller subproblems. The equation that describes the relationship between these subproblems is called the Bellman equation.

Mathematical programming with equilibrium constraints is where the constraints include variational inequalities or complementarities.
Multiobjective optimization
Adding more than one objective to an optimization problem adds complexity. For example, to optimize a structural design, one would desire a design that is both light and rigid. When two objectives conflict, a tradeoff must be created. There may be one lightest design, one stiffest design, and an infinite number of designs that are some compromise of weight and rigidity. The set of tradeoff designs that cannot be improved upon according to one criterion without hurting another criterion is known as the
Pareto set. The curve created plotting weight against stiffness of the best designs is known as the
Pareto frontier.
A design is judged to be "Pareto optimal" (equivalently, "Pareto efficient" or in the Pareto set) if it is not dominated by any other design: If it is worse than another design in some respects and no better in any respect, then it is dominated and is not Pareto optimal.
The choice among "Pareto optimal" solutions to determine the "favorite solution" is delegated to the decision maker. In other words, defining the problem as multiobjective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative to each other. In some cases, the missing information can be derived by interactive sessions with the decision maker.
Multiobjective optimization problems have been generalized further into vector optimization problems where the (partial) ordering is no longer given by the Pareto ordering.
Multimodal optimization
Optimization problems are often multimodal; that is, they possess multiple good solutions. They could all be globally good (same cost function value) or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multimodal optimizer.
Classical optimization techniques due to their iterative approach do not perform satisfactorily when they are used to obtain multiple solutions, since it is not guaranteed that different solutions will be obtained even with different starting points in multiple runs of the algorithm. Evolutionary algorithms, however, are a very popular approach to obtain multiple solutions in a multimodal optimization task.
Classification of critical points and extrema
Feasibility problem
The
satisfiability problem, also called the
feasibility problem, is just the problem of finding any feasible solution at all without regard to objective value. This can be regarded as the special case of mathematical optimization where the objective value is the same for every solution, and thus any solution is optimal.
Many optimization algorithms need to start from a feasible point. One way to obtain such a point is to relax the feasibility conditions using a slack variable; with enough slack, any starting point is feasible. Then, minimize that slack variable until slack is null or negative.
Existence
The extreme value theorem of
Karl Weierstrass states that a continuous realvalued function on a compact set attains its maximum and minimum value. More generally, a lower semicontinuous function on a compact set attains its minimum; an upper semicontinuous function on a compact set attains its maximum.
Necessary conditions for optimality
One of Fermat's theorems states that optima of unconstrained problems are found at
, where the first derivative or the gradient of the objective function is zero (see first derivative test). More generally, they may be found at critical points, where the first derivative or gradient of the objective function is zero or is undefined, or on the boundary of the choice set. An equation (or set of equations) stating that the first derivative(s) equal(s) zero at an interior optimum is called a 'firstorder condition' or a set of firstorder conditions.
Optima of equalityconstrained problems can be found by the Lagrange multiplier method. The optima of problems with equality and/or inequality constraints can be found using the 'Karush–Kuhn–Tucker conditions'.
Sufficient conditions for optimality
While the first derivative test identifies points that might be extrema, this test does not distinguish a point that is a minimum from one that is a maximum or one that is neither. When the objective function is twice differentiable, these cases can be distinguished by checking the second derivative or the matrix of second derivatives (called the
Hessian matrix) in unconstrained problems, or the matrix of second derivatives of the objective function and the constraints called the bordered Hessian in constrained problems. The conditions that distinguish maxima, or minima, from other stationary points are called 'secondorder conditions' (see 'Second derivative test'). If a candidate solution satisfies the firstorder conditions, then satisfaction of the secondorder conditions as well is sufficient to establish at least local optimality.
Sensitivity and continuity of optima
The
envelope theorem describes how the value of an optimal solution changes when an underlying
parameter changes. The process of computing this change is called comparative statics.
The maximum theorem of Claude Berge (1963) describes the continuity of an optimal solution as a function of underlying parameters.
Calculus of optimization
For unconstrained problems with twicedifferentiable functions, some critical points can be found by finding the points where the
gradient of the objective function is zero (that is, the stationary points). More generally, a zero
subgradient certifies that a local minimum has been found for minimization problems with convex
convex function and other locally Lipschitz functions.
Further, critical points can be classified using the definiteness of the Hessian matrix: If the Hessian is positive definite at a critical point, then the point is a local minimum; if the Hessian matrix is negative definite, then the point is a local maximum; finally, if indefinite, then the point is some kind of saddle point.
Constrained problems can often be transformed into unconstrained problems with the help of Lagrange multipliers. Lagrangian relaxation can also provide approximate solutions to difficult constrained problems.
When the objective function is Convex function, then any local minimum will also be a global minimum. There exist efficient numerical techniques for minimizing convex functions, such as interiorpoint methods.
Computational optimization techniques
To solve problems, researchers may use
that terminate in a finite number of steps, or
that converge to a solution (on some specified class of problems), or heuristics that may provide approximate solutions to some problems (although their iterates need not converge).
Optimization algorithms

Simplex algorithm of George Dantzig, designed for linear programming.

Extensions of the simplex algorithm, designed for quadratic programming and for linearfractional programming.

Variants of the simplex algorithm that are especially suited for flow network.

Combinatorial algorithms

Quantum optimization algorithms
Iterative methods
The iterative methods used to solve problems of nonlinear programming differ according to whether they
subroutine Hessian matrix, gradients, or only function values. While evaluating Hessians (H) and gradients (G) improves the rate of convergence, for functions for which these quantities exist and vary sufficiently smoothly, such evaluations increase the computational complexity (or computational cost) of each iteration. In some cases, the computational complexity may be excessively high.
One major criterion for optimizers is just the number of required function evaluations as this often is already a large computational effort, usually much more effort than within the optimizer itself, which mainly has to operate over the N variables.
The derivatives provide detailed information for such optimizers, but are even harder to calculate, e.g. approximating the gradient takes at least N+1 function evaluations. For approximations of the 2nd derivatives (collected in the Hessian matrix) the number of function evaluations is in the order of N². Newton's method requires the 2nd order derivates, so for each iteration the number of function calls is in the order of N², but for a simpler pure gradient optimizer it is only N. However, gradient optimizers need usually more iterations than Newton's algorithm. Which one is best with respect to the number of function calls depends on the problem itself.

Methods that evaluate Hessians (or approximate Hessians, using finite differences):

Newton's method

Sequential quadratic programming: A Newtonbased method for smallmedium scale constrained problems. Some versions can handle largedimensional problems.

Interior point methods: This is a large class of methods for constrained optimization. Some interiorpoint methods use only (sub)gradient information, and others of which require the evaluation of Hessians.

Methods that evaluate gradients, or approximate gradients in some way (or even subgradients):

Coordinate descent methods: Algorithms which update a single coordinate in each iteration

Conjugate gradient methods: for large problems. (In theory, these methods terminate in a finite number of steps with quadratic objective functions, but this finite termination is not observed in practice on finite–precision computers.)

Gradient descent (alternatively, "steepest descent" or "steepest ascent"): A (slow) method of historical and theoretical interest, which has had renewed interest for finding approximate solutions of enormous problems.

Subgradient methods  An iterative method for large locally Lipschitz functions using subgradient. Following Boris T. Polyak, subgradient–projection methods are similar to conjugate–gradient methods.

Bundle method of descent: An iterative method for small–mediumsized problems with locally Lipschitz functions, particularly for convex minimization problems. (Similar to conjugate gradient methods)

Ellipsoid method: An iterative method for small problems with quasiconvex objective functions and of great theoretical interest, particularly in establishing the polynomial time complexity of some combinatorial optimization problems. It has similarities with QuasiNewton methods.

Conditional gradient method (Frank–Wolfe) for approximate minimization of specially structured problems with linear constraints, especially with traffic networks. For general unconstrained problems, this method reduces to the gradient method, which is regarded as obsolete (for almost all problems).

QuasiNewton methods: Iterative methods for mediumlarge problems (e.g. N<1000).

Simultaneous perturbation stochastic approximation (SPSA) method for stochastic optimization; uses random (efficient) gradient approximation.

Methods that evaluate only function values: If a problem is continuously differentiable, then gradients can be approximated using finite differences, in which case a gradientbased method can be used.

Interpolation methods

Pattern search methods, which have better convergence properties than the Nelder–Mead heuristic (with simplices), which is listed below.
Global convergence
More generally, if the objective function is not a quadratic function, then many optimization methods use other methods to ensure that some subsequence of iterations converges to an optimal solution. The first and still popular method for ensuring convergence relies on
, which optimize a function along one dimension. A second and increasingly popular method for ensuring convergence uses
. Both line searches and trust regions are used in modern methods of nondifferentiable optimization. Usually a global optimizer is much slower than advanced local optimizers (such as
BFGS method), so often an efficient global optimizer can be constructed by starting the local optimizer from different starting points.
Heuristics
Besides (finitely terminating)
and (convergent)
, there are heuristics. A heuristic is any algorithm which is not guaranteed (mathematically) to find the solution, but which is nevertheless useful in certain practical situations. List of some wellknown heuristics:

Memetic algorithm

Differential evolution

Evolutionary algorithms

Dynamic relaxation

Genetic algorithms

Hill climbing with random restart

NelderMead simplicial heuristic: A popular heuristic for approximate minimization (without calling gradients)

Particle swarm optimization

Gravitational search algorithm

Artificial bee colony optimization

Simulated annealing

Stochastic tunneling

Tabu search

Reactive Search Optimization (RSO)
[
]
implemented in
LIONsolver
Applications
Mechanics
Problems in rigid body dynamics (in particular articulated rigid body dynamics) often require mathematical programming techniques, since you can view rigid body dynamics as attempting to solve an ordinary differential equation on a constraint manifold; the constraints are various nonlinear geometric constraints such as "these two points must always coincide", "this surface must not penetrate any other", or "this point must always lie somewhere on this curve". Also, the problem of computing contact forces can be done by solving a linear complementarity problem, which can also be viewed as a QP (quadratic programming) problem.
Many design problems can also be expressed as optimization programs. This application is called design optimization. One subset is the engineering optimization, and another recent and growing subset of this field is multidisciplinary design optimization, which, while useful in many problems, has in particular been applied to aerospace engineering problems.
This approach may be applied in cosmology and astrophysics,.
Economics and finance
Economics is closely enough linked to optimization of agents that an influential definition relatedly describes economics
qua science as the "study of human behavior as a relationship between ends and
scarce means" with alternative uses.
[Lionel Robbins (1935, 2nd ed.) An Essay on the Nature and Significance of Economic Science, Macmillan, p. 16.] Modern optimization theory includes traditional optimization theory but also overlaps with
game theory and the study of economic equilibria. The
Journal of Economic Literature codes classify mathematical programming, optimization techniques, and related topics under .
In microeconomics, the utility maximization problem and its dual problem, the expenditure minimization problem, are economic optimization problems. Insofar as they behave consistently, are assumed to maximize their utility, while are usually assumed to maximize their profit. Also, agents are often modeled as being Risk aversion, thereby preferring to avoid risk. Asset pricing are also modeled using optimization theory, though the underlying mathematics relies on optimizing stochastic processes rather than on static optimization. International trade theory also uses optimization to explain trade patterns between nations. The optimization of portfolios is an example of multiobjective optimization in economics.
Since the 1970s, economists have modeled dynamic decisions over time using control theory. For example, dynamic search theory are used to study labor economics. A crucial distinction is between deterministic and stochastic models.[A.G. Malliaris (2008). "stochastic optimal control," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.] Macroeconomics build dynamic stochastic general equilibrium (DSGE) models that describe the dynamics of the whole economy as the result of the interdependent optimizing decisions of workers, consumers, investors, and governments.[From The New Palgrave Dictionary of Economics (2008), 2nd Edition with Abstract links:]
• " numerical optimization methods in economics" by Karl Schmedders
• " convex programming" by Lawrence E. Blume
• " Arrow–Debreu model of general equilibrium" by John Geanakoplos.
Electrical engineering
Some common applications of optimization techniques in electrical engineering include
active filter design,
stray field reduction in superconducting magnetic energy storage systems,
space mapping design of
microwave structures,
[S. Koziel and J.W. Bandler, "Space mapping with multiple coarse models for optimization of microwave components," IEEE Microwave and Wireless Components Letters, vol. 8, no. 1, pp. 1–3, Jan. 2008.] handset antennas,
[S. Tu, Q.S. Cheng, Y. Zhang, J.W. Bandler, and N.K. Nikolova, “Space mapping optimization of handset antennas exploiting thinwire models,” IEEE Trans. Antennas Propag., vol. 61, no. 7, pp. 37973807, July 2013.]][N. Friedrich, “Space mapping outpaces EM optimization in handsetantenna design,” microwaves&rf, Aug. 30, 2013.][Juan C. CervantesGonzález, J. E. RayasSánchez, C. A. López, J. R. CamachoPérez, Z. BritoBrito, and J. L. ChavezHurtado, “Space mapping optimization of handset antennas considering EM effects of mobile phone components and human body,” Int. J. RF and Microwave CAE, vol. 26, no. 2, pp. 121128, Feb. 2016] electromagneticsbased design. Electromagnetically validated design optimization of microwave components and antennas has made extensive use of an appropriate physicsbased or empirical
surrogate model and
space mapping methodologies since the discovery of
space mapping in 1993.
[J.W. Bandler, R.M. Biernacki, S.H. Chen, P.A. Grobelny, and R.H. Hemmers, “Space mapping technique for electromagnetic optimization,” IEEE Trans. Microwave Theory Tech., vol. 42, no. 12, pp. 25362544, Dec. 1994.][J.W. Bandler, R.M. Biernacki, S.H. Chen, R.H. Hemmers, and K. Madsen, “Electromagnetic optimization exploiting aggressive space mapping,” IEEE Trans. Microwave Theory Tech., vol. 43, no. 12, pp. 28742882, Dec. 1995.]
Civil engineering
Optimization has been widely used in civil engineering. The most common civil engineering problems that are solved by optimization are cut and fill of roads, lifecycle analysis of structures and infrastructures,
[ Piryonesi, S. M., & Tavakolan, M. (2017). A mathematical programming model for solving costsafety optimization (CSO) problems in the maintenance of structures. KSCE Journal of Civil Engineering, 110.] resource leveling
[Hegazy, T., (1999) Optimization of Resource Allocation and Leveling Using Genetic Algorithms, Journal of Construction Engineering and Management, ASCE, Vol. 125, No. 3, pp 167175.] and schedule optimization.
Operations research
Another field that uses optimization techniques extensively is operations research.
Operations research also uses stochastic modeling and simulation to support improved decisionmaking. Increasingly, operations research uses stochastic programming to model dynamic decisions that adapt to events; such problems can be solved with largescale optimization and stochastic optimization methods.
Control engineering
Mathematical optimization is used in much modern controller design. Highlevel controllers such as model predictive control (MPC) or realtime optimization (RTO) employ mathematical optimization. These algorithms run online and repeatedly determine values for decision variables, such as choke openings in a process plant, by iteratively solving a mathematical optimization problem including constraints and a model of the system to be controlled.
Geophysics
Optimization techniques are regularly used in
geophysics parameter estimation problems. Given a set of geophysical measurements, e.g.
seismology, it is common to solve for the
mineral physics and geometrical shapes of the underlying rocks and fluids.
Molecular modeling
Nonlinear optimization methods are widely used in conformational analysis.
Solvers
See also
Notes
Further reading
Comprehensive
Undergraduate level
Graduate level




J. E. Dennis, Jr. and Robert B. Schnabel, A view of unconstrained optimization (pp. 1–72);

Donald Goldfarb and Michael J. Todd, Linear programming (pp. 73–170);

Philip E. Gill, Walter Murray, Michael A. Saunders, and Margaret H. Wright, Constrained nonlinear programming (pp. 171–210);

Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin, Network flows (pp. 211–369);

W. R. Pulleyblank, Polyhedral combinatorics (pp. 371–446);

George L. Nemhauser and Laurence A. Wolsey, Integer programming (pp. 447–527);

Claude Lemaréchal, Nondifferentiable optimization (pp. 529–572);

Roger JB Wets, Stochastic programming (pp. 573–629);

A. H. G. Rinnooy Kan and G. T. Timmer, Global optimization (pp. 631–662);

P. L. Yu, Multiple criteria decision making: five basic concepts (pp. 663–699).


Spall, J. C. (2003), Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Wiley, Hoboken, NJ.

Continuous optimization
Combinatorial optimization

R. K. Ahuja, Thomas L. Magnanti, and James B. Orlin (1993). Network Flows: Theory, Algorithms, and Applications. PrenticeHall, Inc. .

William J. Cook, William H. Cunningham, William R. Pulleyblank, Alexander Schrijver; Combinatorial Optimization; John Wiley & Sons; 1 edition (November 12, 1997); .



.

Jon Lee; A First Course in Combinatorial Optimization; Cambridge University Press; 2004; .

Christos H. Papadimitriou and Kenneth Steiglitz Combinatorial Optimization : Algorithms and Complexity; Dover Pubns; (paperback, Unabridged edition, July 1998) .
Relaxation (extension method)
Methods to obtain suitable (in some sense) natural extensions of optimization problems that otherwise lack of existence or stability of solutions to obtain problems with guaranteed existence of solutions and their stability in some sense (typically under various perturbation of data) are in general called relaxation. Solutions of such extended (=relaxed) problems in some sense characterizes (at least certain features) of the original problems, e.g. as far as their optimizing sequences concerns. Relaxed problems may also possesses their own natural linear structure that may yield specific optimality conditions different from optimality conditions for the original problems.

H. O. Fattorini: Infinite Dimensional Optimization and Control Theory. Cambridge Univ. Press, 1999.

P. Pedregal: Parametrized Measures and Variational Principles. Birkhäuser, Basel, 1997

T. Roubicek: "Relaxation in Optimization Theory and Variational Calculus". W. de Gruyter, Berlin, 1997. .

J. Warga: Optimal control of differential and functional equations. Academic Press, 1972.
Journals
External links