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ConvexOptimization—Wolfram Language Documentation

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• See Also
  • LinearOptimization
  • QuadraticOptimization
  • SecondOrderConeOptimization
  • SemidefiniteOptimization
  • ConicOptimization
  • GeometricOptimization
  • LinearFractionalOptimization
• Related Guides
  • Convex Optimization
  • Optimization
  • Symbolic Vectors, Matrices and Arrays
• Tech Notes
  • Optimization Method Framework

ConvexOptimization[f,cons,vars]

finds values of variables vars that minimize the convex objective function f subject to convex constraints cons.

• Convex optimization is global nonlinear optimization for convex functions with convex constraints. For convex problems, the global solution can be found.
• Convex optimization includes many other forms of optimization, including linear optimization, linear-fractional optimization, quadratic optimization, second-order cone optimization, semidefinite optimization and conic optimization.
• If is concave, ConvexOptimization[-g,cons,vars] will maximize g.
• Convex optimization finds that solves the following problem:
• Equality constraints of the form may be included in cons.
• Mixed-integer convex optimization finds and that solve the problem:
• When the objective function is real valued, ConvexOptimization solves problems with by internally converting to real variables , where and .
• The variable specification vars should be a list with elements giving variables in one of the following forms:
• ConvexOptimization automatically does transformations necessary to find an efficient method to solve the minimization problem.
• The primal minimization problem as solved has a related maximization problem that is the Lagrangian dual problem. The dual maximum value is always less than or equal to the primal minimum value, so it provides a lower bound. The dual maximizer provides information about the primal problem, including sensitivity of the minimum value to changes in the constraints.
• The possible solution properties "prop" include:
• The following options may be given:
• The option Methodmethod may be used to specify the method to use. Available methods include:
• Automatic choose the method automatically solver transform the problem, if possible, to use solver to solve the problem "SCS" SCS splitting conic solver "CSDP" CSDP semidefinite optimization solver "DSDP" DSDP semidefinite optimization solver "MOSEK" commercial MOSEK convex optimization solver "Gurobi" commercial Gurobi linear and quadratic optimization solver "Xpress" commercial Xpress linear and quadratic optimization solver
• Methodsolver may be used to specify that a particular solver is used so that the dual formulation used corresponds to the formulation documented for solver. Possible solvers are LinearOptimization, LinearFractionalOptimization, QuadraticOptimization, SecondOrderConeOptimization, SemidefiniteOptimization, ConicOptimization and GeometricOptimization.

Minimize subject to linear constraints:

Minimize a matrix norm subject to constraints on some elements:

Minimize subject to the constraints and :

Several linear inequality constraints can be expressed with VectorGreaterEqual:

Use v>= or \[VectorGreaterEqual] to enter the vector inequality sign :

An equivalent form using scalar inequalities:

Use a vector variable :

The inequality may not be the same as due to possible threading in :

To avoid unintended threading in , use Inactive[Plus]:

Use constant parameter equations to avoid unintended threading in :

VectorGreaterEqual represents a conic inequality with respect to the "NonNegativeCone":

To explicitly specify the dimension of the cone, use {"NonNegativeCone",n}:

Find the solution:

Minimize subject to the constraint :

Specify the constraint using a conic inequality with "NormCone":

Find the solution:

Minimize subject to the positive semidefinite matrix constraint :

Find the solution:

Use a vector variable and Indexed[x,i] to specify individual components:

Use Vectors[n,Reals] to specify the dimension of a vector variable when it may be ambiguous:

Specify non-negative constraints using NonNegativeReals ():

An equivalent form using vector inequality :

Maximize the area of a rectangle with perimeter at most 1 and height at most half the width:

When and are positive, the problem can be solved by GeometricOptimization methods:

Using method GeometricOptimization implicitly assumes positivity:

Specify complex variables using Complexes:

Minimize a real objective with complex variables and complex constraints :

Let . Expanding out the constraints into real components gives:

Solve the problem with real-valued objective and complex variables and constraints:

Solve the same problem with real variables and constraints:

Use a quadratic objective with Hermitian matrix and real-valued variables:

Use objective (1/2)Inactive[Dot][Conjugate[x],q,x] with a Hermitian matrix and complex variables:

Use a quadratic constraint with Hermitian matrix and real-valued variables:

Use constraint (1/2)Inactive[Dot][Conjugate[x],q,x]d with a Hermitian matrix and complex variables:

Find the Hermitian matrix with minimum 2-norm (largest singular value) such that the matrix is positive semidefinite:

The minimum for the largest singular value is:

Use a linear matrix inequality constraint with Hermitian or real symmetric matrices:

The variables in linear matrix inequalities need to be real for the sum to remain Hermitian:

Minimize over the intersection of a triangle and a disk :

Get the primal minimizer as a vector:

Get the minimal value:

Plot the solution:

Minimize subject to and :

The dual problem is to maximize subject to :

The primal minimum value and the dual maximum value coincide because of strong duality:

That is the same as having a duality gap of zero. In general, at optimal points:

Get the dual maximum value and dual maximizer directly using solution properties:

The "DualMaximizer" can be obtained with:

The dual maximizer vector partitions match the number and dimensions of the dual cones:

To get the dual format for a particular problem-type solver, specify it as a method option:

"SCS" is a splitting conic solver method:

"CSDP" is an interior point method for semidefinite problems:

"DSDP" is an alternative interior point method for semidefinite problems:

"IPOPT" is an interior point method for nonlinear problems:

Different methods have different default tolerances, which affects the accuracy and precision:

Compute exact and approximate solutions:

"SCS" has a default tolerance of :

"CSDP", "DSDP" and "IPOPT" have default tolerances of :

When method "SCS" is specified, it is called with the SCS library default tolerance of 10^-3:

With default options, this problem is solved by method "SCS" with tolerance 10^-6:

Use methods "CSDP" or "DSDP" for constraints that are converted to semidefinite constraints:

Solve the problem using method "CSDP":

Solve the problem using method "DSDP":

Use method "IPOPT" to obtain accurate solutions when "CSDP" and "DSDP" are not applicable:

"IPOPT" produces more accurate results than "SCS", but is typically much slower:

Compare timing with method "SCS":

A smaller Tolerance setting gives a more precise result:

Compute the exact minimum value with Minimize:

Compute the error in the minimum value with different Tolerance settings:

Visualize the change in minimum value error with respect to tolerance:

A smaller Tolerance setting gives a more precise answer, but may take longer to compute:

The tighter tolerance gives a more precise answer:

The default working precision is MachinePrecision:

Using WorkingPrecisionInfinity will give an exact solution if possible:

WorkingPrecision other than MachinePrecision and ∞ will try to use a method with extended precision support:

Using WorkingPrecisionAutomatic will try to use the precision of the input problem:

Solve a problem with a quadratic objective using 24-digit precision:

There is currently no method that solves problems with quadratic objectives using exact arithmetic. When the requested precision is not supported, the computation uses machine numbers:

Maximize subject to . Solve a maximization problem by negating the objective function:

Negate the primal minimum value to get the corresponding maximal value:

Minimize subject to . Since the constraint is not convex, use a semidefinite constraint to make the convexity explicit:

A matrix is positive semidefinite if and only if the determinants of all upper-left submatrices are non-negative:

Find the solution:

Minimize subject to , assuming when . Using the auxiliary variable , the objective is to minimize such that :

Check that implies :

A Schur complement condition says that if , a block matrix iff . Therefore, iff . Use Inactive[Plus] for constructing the constraints to avoid threading:

Minimize over an ellipse centered at :

The epigraph transformation can be used to construct a problem with a linear objective and additional variable and constraint:

In this form, the problem can be solved directly with ConicOptimization:

Minimize , where is a nondecreasing function, by instead minimizing . The primal minimizer will remain the same for both problems. Consider minimizing subject to :

The minimum value for can be obtained by applying to the minimum value of :

ConvexOptimization will automatically do this transformation:

Find that minimizes the largest eigenvalue of a symmetric matrix that depends linearly on the decision variables , . The problem can be formulated as a linear matrix inequality since is equivalent to , where is the eigenvalue of . Define the linear matrix function :

A real symmetric matrix can be diagonalized with an orthogonal matrix so . Hence iff . Since any , taking , , hence iff . Numerically simulate to show that these formulations are equivalent:

The resulting problem:

Run a Monte Carlo simulation to check the plausibility of the result:

Find that maximizes the smallest eigenvalue of a symmetric matrix that depends linearly on the decision variables . Define the linear matrix function :

The problem can be formulated as linear matrix inequality, since is equivalent to where is the eigenvalue of . To maximize , minimize :

Run a Monte Carlo simulation to check the plausibility of the result:

Find that minimizes the difference between the largest and the smallest eigenvalues of a symmetric matrix that depends linearly on the decision variables . Define the linear matrix function :

The problem can be formulated as a linear matrix inequality, since is equivalent to , where is the eigenvalue of . Solve the resulting problem:

In this case, the minimum and maximum eigenvalues coincide and the difference is 0:

Minimize the largest (by absolute value) eigenvalue of a symmetric matrix that depends linearly on the decision variables :

The largest eigenvalue satisfies The largest (by absolute value) negative eigenvalue of is the largest eigenvalue of and satisfies :

Find that minimizes the largest singular value of a symmetric matrix that depends linearly on the decision variables :

The largest singular value of is the square root of the largest eigenvalue of , and from a preceding example it satisfies , or equivalently :

Plot the result:

For quadratic sets , which include ellipsoids, quadratic cones and paraboloids, determine whether , where are symmetric matrices, are vectors and scalars:

Assuming that the sets are full dimensional, the S-procedure says that iff there exists some non-negative number such that Visually see that there exists a non-negative :

Use 0 for an objective function since feasibility is a concern. Since λ≥0, it follows that :

Minimize the length of the diagonal of a rectangle of area 4 such that the width plus three times the height is less than 7:

Find the minimum distance between two disks of radius 1 centered at and . Let be a point on disk 1. Let be a point on disk 2. The objective is to minimize subject to constraints :

Visualize the positions of the two points:

The distance between the points is:

Find the half-lengths of the principal axes that maximize the volume of an ellipsoid with a surface area of at most 1:

The surface area can be approximated by:

Maximize the volume area by minimizing its reciprocal:

This is the sphere. Including additional constraints on the axes lengths changes this:

Find the radius and center of a minimal enclosing ball that encompasses a given region:

Minimize the radius subject to the constraints :

Visualize the enclosing ball:

The minimal enclosing ball can be found efficiently using BoundingRegion:

Find the analytic center of a convex polygon. The analytic center is a point that maximizes the product of distances to the constraints:

Each segment of the convex polygon can be represented as intersections of half-planes . Extract the linear inequalities:

The objective is to maximize . Taking and negating the objective, the transformed objective is :

Using auxiliary variable , the transformed objective is subject to the constraint :

Visualize the location of the center:

Test whether an ellipsoid is a subset of another ellipsoid of the form :

Using the S-procedure, it can be shown that ellipse 2 is a subset of ellipse 1 iff :

Check if the condition is satisfied:

Convert the ellipsoids into explicit form and confirm that ellipse 2 is within ellipse 1:

Move ellipsoid 2 such that it overlaps with ellipsoid 1:

A test now shows that the problem is infeasible, indicating that ellipsoid 2 is not a subset of ellipsoid 1:

Find the maximum-area ellipse parametrized as that can be fitted into a convex polygon:

Each segment of the convex polygon can be represented as intersections of half-planes . Extract the linear inequalities:

Applying the parametrization to the half-planes gives . The term . Thus, the constraints are:

Minimizing the area is equivalent to minimizing , which is equivalent to minimizing :

Convert the parametrized ellipse into the explicit form as :

Find the smallest ellipsoid parametrized as that encompasses a set of points in 3D by minimizing the volume:

For each point , the constraint must be satisfied:

Minimizing the volume is equivalent to minimizing , which is equivalent to minimizing :

Convert the parametrized ellipse into the explicit form :

A bounding ellipsoid, not necessarily minimum volume, can also be found using BoundingRegion:

Minimize subject to the constraints for a given matrix a and vector b:

Fit a cubic curve to discrete data such that the first and last points of the data lie on the curve:

Construct the matrix using DesignMatrix:

Define the constraint so that the first and last points must lie on the curve:

Find the coefficients by minimizing :

Compare fit with data:

Find a fit less sensitive to outliers to nonlinear discrete data by minimizing :

Fit the data using the bases . The interpolating function will be :

Find the solution:

Visualize the fit:

Compare the interpolating function with the reference function:

Find an regularized fit to complex data by minimizing for a complex :

Construct the matrix using DesignMatrix, for the basis :

Find the coefficients :

Let be the fit defined as a function of the real and imaginary components of :

Visualize the result for the real component of :

Visualize the results for the imaginary component of :

Represent a given polynomial in terms of the sum-of-squares polynomial :

The objective is to find such that , where is a vector of monomials:

Construct the symmetric matrix :

Find the polynomial coefficients of and and make sure they are equal:

Find the elements of :

The quadratic term , where is a lower-triangular matrix obtained from the Cholesky decomposition of :

Compare the sum-of-squares polynomial to the given polynomial:

Find a line that separates two groups of points and :

For separation, set 1 must satisfy and set 2 must satisfy :

The objective is to minimize , which gives twice the thickness between and :

The separating line is:

Find a quadratic polynomial that separates two groups of 3D points and :

Construct the quadratic polynomial data matrices for the two sets using DesignMatrix:

For separation, set 1 must satisfy and set 2 must satisfy :

Find the separating polynomial by minimizing :

The polynomial separating the two groups of points is:

Plot the polynomial separating the two datasets:

Separate a given set of points into different groups. This is done by finding the centers for each group by minimizing , where is a given local kernel and is a given penalty parameter:

The kernel is a -nearest neighbor () function such that , else . For this problem, nearest neighbors are selected:

The objective is:

Find the group centers:

For each data point, there exists a corresponding center. Data belonging to the same group will have the same center value:

Extract and plot the grouped points:

Find the positions of various cell towers and the range needed to serve clients located at :

Each cell tower consumes power proportional to its range, which is given by . The objective is to minimize the power consumption:

Let be a decision variable indicating that if client is covered by cell tower :

Each cell tower must be located such that its range covers some of the clients:

Each cell tower can cover multiple clients:

Each cell tower has a minimum and maximum coverage:

Collect all the variables:

Find the cell tower positions and their ranges:

Extract cell tower position and range:

Visualize the positions and ranges of the towers with respect to client locations:

Find the distribution of capital to invest in six stocks to maximize return while minimizing risk:

The return is given by , where is a vector of expected return value of each individual stock:

The risk is given by ; is a risk-aversion parameter and :

The objective is to maximize return while minimizing risk for a specified risk-aversion parameter:

The effect on market prices of stocks due to the buying and selling of stocks is modeled by , which is modeled by a power cone using the epigraph transformation:

The weights must all be greater than 0 and the weights plus market impact costs must add to 1:

Compute the returns and corresponding risk for a range of risk-aversion parameters:

The optimal over a range of gives an upper-bound envelope on the tradeoff between return and risk:

Compute the weights for a specified number of risk-aversion parameters:

By accounting for the market costs, a diversified portfolio can be obtained for low risk aversion, but when the risk aversion is high, the market impact cost dominates, due to purchasing a less diversified stock:

Recover a corrupted image by finding an image that is closest under the total variation norm:

Create a corrupted image by randomly deleting 40% of the data points.

The objective is to minimize , where is the image data:

Assume that any nonzero data points are uncorrupted. For these positions, set :

Find the solution and show the restored image:

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