Semidefinite programming is a relatively new field of optimization which is of growing interest for several reasons. Many practical problems in
operations research and
combinatorial optimization can be modeled or approximated as semidefinite programming problems. In automatic control theory, SDPs are used in the context of
linear matrix inequalities. SDPs are in fact a special case of
cone programming and can be efficiently solved by
interior point methods.
linear programs and (convex)
quadratic programs can be expressed as SDPs, and via hierarchies of SDPs the solutions of polynomial optimization problems can be approximated. Semidefinite programming has been used in the
optimization of complex systems. In recent years, some quantum query complexity problems have been formulated in terms of semidefinite programs.
Motivation and definition
linear programming problem is one in which we wish to maximize or minimize a linear objective function of real variables over a
polytope. In semidefinite programming, we instead use real-valued vectors and are allowed to take the dot product of vectors; nonnegativity constraints on real variables in LP (linear programming) are replaced by semidefiniteness constraints on matrix variables in SDP (semidefinite programming). Specifically, a general semidefinite programming problem can be defined as any mathematical programming problem of the form
where the , and the are real numbers and is the
dot product of and .
An matrix is said to be
positive semidefinite if it is the
Gram matrix of some vectors (i.e. if there exist vectors such that for all ). If this is the case, we denote this as . Note that there are several other equivalent definitions of being positive semidefinite, for example, positive semidefinite matrices are
self-adjoint matrices that have only non-negative
Denote by the space of all real symmetric matrices. The space is equipped with the
inner product (where denotes the
We can rewrite the mathematical program given in the previous section equivalently as
where entry in is given by from the previous section and is a symmetric matrix having th entry from the previous section. Thus, the matrices and are symmetric and the above inner products are well-defined.
Note that if we add
slack variables appropriately, this SDP can be converted to one of the form
For convenience, an SDP may be specified in a slightly different, but equivalent form. For example, linear expressions involving nonnegative
scalar variables may be added to the program specification. This remains an SDP because each variable can be incorporated into the matrix as a diagonal entry ( for some ). To ensure that , constraints can be added for all . As another example, note that for any positive semidefinite matrix , there exists a set of vectors such that the , entry of is the
scalar product of and . Therefore, SDPs are often formulated in terms of linear expressions on scalar products of vectors. Given the solution to the SDP in the standard form, the vectors can be recovered in time (e.g., by using an incomplete
Cholesky decomposition of X).
Analogously to linear programming, given a general SDP of the form
(the primal problem or P-SDP), we define the dual semidefinite program (D-SDP) as
where for any two matrices and , means .
weak duality theorem states that the value of the primal SDP is at least the value of the dual SDP. Therefore, any feasible solution to the dual SDP lower-bounds the primal SDP value, and conversely, any feasible solution to the primal SDP upper-bounds the dual SDP value. This is because
where the last inequality is because both matrices are positive semidefinite, and the result of this function is sometimes referred to as duality gap.
When the value of the primal and dual SDPs are equal, the SDP is said to satisfy the
strong duality property. Unlike
linear programs, where every dual linear program has optimal objective equal to the primal objective, not every SDP satisfies strong duality; in general, the value of the dual SDP may lie strictly below the value of the primal, and the P-SDP and D-SPD satisfy the following properties:
(i) Suppose the primal problem (P-SDP) is bounded below and strictly
feasible (i.e., there exists
such that , ).
Then there is an optimal solution to (D-SDP) and
(ii) Suppose the dual problem (D-SDP) is bounded above and strictly
for some ).
Then there is an optimal solution to (P-SDP) and
the equality from (i) holds.
A sufficient condition for strong duality to hold for a SDP problem (and in general, for any convex optimization problem) is the
Slater's condition. It is also possible to attain strong duality for SDPs without additional regularity conditions by using an extended dual problem proposed by Ramana.
in which case this matrix is called the
correlation matrix. Suppose that we know from some prior knowledge (empirical results of an experiment, for example) that and . The problem of determining the smallest and largest values that can take is given by:
We set to obtain the answer. This can be formulated by an SDP. We handle the inequality constraints by augmenting the variable matrix and introducing
slack variables, for example
Solving this SDP gives the minimum and maximum values of as and respectively.
Consider the problem
where we assume that whenever .
Introducing an auxiliary variable the problem can be reformulated:
In this formulation, the objective is a linear function of the variables .
The first restriction can be written as
where the matrix is the square matrix with values in the diagonal equal
to the elements of the vector .
The second restriction can be written as
Defining as follows
We can use the theory of Schur Complements to see that
(Boyd and Vandenberghe, 1996)
The semidefinite program associated with this problem is
Example 3 (Goemans–Williamson max cut approximation algorithm)
Semidefinite programs are important tools for developing approximation algorithms for NP-hard maximization problems. The first approximation algorithm based on an SDP is due to
Michel Goemans and
David P. Williamson (JACM, 1995). They studied the
max cut problem: Given a
graphG = (V, E), output a
partition of the vertices V so as to maximize the number of edges crossing from one side to the other. This problem can be expressed as an
integer quadratic program:
Maximize such that each .
P = NP, we cannot solve this maximization problem efficiently. However, Goemans and Williamson observed a general three-step procedure for attacking this sort of problem:
Relax the integer quadratic program into an SDP.
Solve the SDP (to within an arbitrarily small additive error ).
Round the SDP solution to obtain an approximate solution to the original integer quadratic program.
For max cut, the most natural relaxation is
such that , where the maximization is over vectors instead of integer scalars.
This is an SDP because the objective function and constraints are all linear functions of vector inner products. Solving the SDP gives a set of unit vectors in ; since the vectors are not required to be collinear, the value of this relaxed program can only be higher than the value of the original quadratic integer program. Finally, a rounding procedure is needed to obtain a partition. Goemans and Williamson simply choose a uniformly random hyperplane through the origin and divide the vertices according to which side of the hyperplane the corresponding vectors lie. Straightforward analysis shows that this procedure achieves an expected approximation ratio (performance guarantee) of 0.87856 - ε. (The expected value of the cut is the sum over edges of the probability that the edge is cut, which is proportional to the angle between the vectors at the endpoints of the edge over . Comparing this probability to , in expectation the ratio is always at least 0.87856.) Assuming the
unique games conjecture, it can be shown that this approximation ratio is essentially optimal.
Since the original paper of Goemans and Williamson, SDPs have been applied to develop numerous approximation algorithms. Recently, Prasad Raghavendra has developed a general framework for constraint satisfaction problems based on the
unique games conjecture.
There are several types of algorithms for solving SDPs. These algorithms output the value of the SDP up to an additive error in time that is polynomial in the program description size and .
There are also facial reduction algorithms that can be used to preprocess SDPs problems by inspecting the constraints of the problem. These can be used to detect lack of strict feasibility, to delete redundant rows and columns, and also to reduce the size of the variable matrix.
Interior point methods
Most codes are based on
interior point methods (CSDP,
SDPT3, DSDP, SDPA). These are robust and efficient for general linear SDP problems, but restricted by the fact that the algorithms are second-order methods and need to store and factorize a large (and often dense) matrix. Theoretically, the state-of-the-art high-accuracy SDP algorithms are based on this approach.
First-order methods for
conic optimization avoid computing, storing and factorizing a large Hessian matrix and scale to much larger problems than interior point methods, at some cost in accuracy. A first-order method is implemented in the Splitting Cone Solver (SCS). Another first-order method is the
alternating direction method of multipliers (ADMM). This method requires in every step projection on the cone of semidefinite matrices.
The code ConicBundle formulates the SDP problem as a
nonsmooth optimization problem and solves it by the Spectral Bundle method of nonsmooth optimization. This approach is very efficient for a special class of linear SDP problems.
Other solving methods
Algorithms based on
Augmented Lagrangian method (PENSDP) are similar in behavior to the interior point methods and can be specialized to some very large scale problems. Other algorithms use low-rank information and reformulation of the SDP as a
nonlinear programming problem (SDPLR).
Algorithms that solve SDPs approximately have been proposed as well. The main goal of such methods is to achieve lower complexity in applications where approximate solutions are sufficient and complexity must be minimal. A prominent method that has been used for data detection in multiple-input multiple-output (MIMO) wireless systems is Triangular Approximate SEmidefinite Relaxation (TASER), which operates on the Cholesky decomposition factors of the semidefinite matrix instead of the semidefinite matrix. This method calculates approximate solutions for a max-cut-like problem that are often comparable to solutions from exact solvers but in only 10-20 algorithm iterations.
Semidefinite programming has been applied to find approximate solutions to combinatorial optimization problems, such as the solution of the
max cut problem with an
approximation ratio of 0.87856. SDPs are also used in geometry to determine tensegrity graphs, and arise in control theory as
LMIs, and in inverse elliptic coefficient problems as convex, non-linear, semidefiniteness constraints. It is also widely used in physics to constrain
conformal field theories with the
^Brendan O'Donoghue, Eric Chu,
Neal Parikh, Stephen Boyd, "Conic Optimization via Operator Splitting and
Homogeneous Self-Dual Embedding",
Journal of Optimization Theory and Applications,
^Wen, Zaiwen, Donald Goldfarb, and Wotao Yin. "Alternating direction augmented Lagrangian methods for semidefinite programming." Mathematical Programming Computation 2.3-4 (2010): 203-230.