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Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard.
Convex optimization has applications in a wide range of disciplines, such as automatic control systems, estimation and signal processing, communications and networks, electronic circuit design, data analysis and modeling, finance, statistics (optimal experimental design), and structural optimization. With recent advancements in computing and optimization algorithms, convex programming is nearly as straightforward as linear programming.
A convex optimization problem is an optimization problem in which the objective function is a convex function and the feasible set is a convex set. A function mapping some subset of into is convex if its domain is convex and for all and all in its domain, the following condition holds: . A set S is convex if for all members and all , we have that .
Concretely, a convex optimization problem is the problem of finding some attaining
where the objective function is convex, as is the feasible set .  If such a point exists, it is referred to as an optimal point; the set of all optimal points is called the optimal set. If is unbounded below over or the infimum is not attained, then the optimization problem is said to be unbounded. Otherwise, if is the empty set, then the problem is said to be infeasible.
A convex optimization problem is said to be in the standard form if it is written as
where is the optimization variable, the functions are convex, and the functions are affine.  In this notation, the function is the objective function of the problem, and the functions and are referred to as the constraint functions. The feasible set of the optimization problem is the set consisting of all points satisfying and . This set is convex because the sublevel sets of convex functions are convex, affine sets are convex, and the intersection of convex sets is convex. 
Many optimization problems can be equivalently formulated in this standard form. For example, the problem of maximizing a concave function can be re-formulated equivalently as the problem of minimizing the convex function ; as such, the problem of maximizing a concave function over a convex set is often referred to as a convex optimization problem.
- every local minimum is a global minimum;
- the optimal set is convex;
- if the objective function is strictly convex, then the problem has at most one optimal point.
These results are used by the theory of convex minimization along with geometric notions from functional analysis (in Hilbert spaces) such as the Hilbert projection theorem, the separating hyperplane theorem, and Farkas' lemma.
Consider a convex minimization problem given in standard form by a cost function and inequality constraints for . Then the domain is:
The Lagrangian function for the problem is
For each point in that minimizes over , there exist real numbers called Lagrange multipliers, that satisfy these conditions simultaneously:
- minimizes over all
- with at least one
- (complementary slackness).
If there exists a "strictly feasible point", that is, a point satisfying
then the statement above can be strengthened to require that .
Conversely, if some in satisfies (1)–(3) for scalars with then is certain to minimize over .
Convex optimization problems can be solved by the following contemporary methods:
- Bundle methods (Wolfe, Lemaréchal, Kiwiel), and
- Subgradient projection methods (Polyak),
- Interior-point methods, which make use of self-concordant barrier functions  and self-regular barrier functions.
- Cutting-plane methods
- Ellipsoid method
- Subgradient method
- Dual subgradients and the drift-plus-penalty method
Subgradient methods can be implemented simply and so are widely used. Dual subgradient methods are subgradient methods applied to a dual problem. The drift-plus-penalty method is similar to the dual subgradient method, but takes a time average of the primal variables.
Extensions of convex optimization include the optimization of biconvex, pseudo-convex, and quasiconvex functions. Extensions of the theory of convex analysis and iterative methods for approximately solving non-convex minimization problems occur in the field of generalized convexity, also known as abstract convex analysis.
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