SemidefiniteOptimization

SemidefiniteOptimization[f,cons,vars]

finds values of variables vars that minimize the linear objective f subject to semidefinite constraints cons.

SemidefiniteOptimization[c,{a0,a1,,ak}]

finds a vector that minimizes the quantity subject to the linear matrix inequality constraint a_0+a_1 x_1+...+a_k x_k>=_(TemplateBox[{n}, SemidefiniteConeList])0.

SemidefiniteOptimization[,"prop"]

specifies what solution property "prop" should be returned.

Details and Options

  • SemidefiniteOptimization is also known as semidefinite programming (SDP).
  • Semidefinite optimization is a convex optimization problem that can be solved globally and efficiently.
  • Semidefinite optimization finds that solves the primal problem:
  • minimize
    subject to constraintsa_(0)+a_(1) x_(1)+...+a_(k) x_(k)>=_(TemplateBox[{n}, SemidefiniteConeList])0
    where
  • The matrices must be symmetric matrices.
  • Mixed-integer semidefinite optimization finds and that solve the problem:
  • minimize
    subject to constraintsa_(0)+a_(1) x_(1)+...+a_(k_r) x_(k_r)+a_(k_r+1)y_1+...+a_(k_r+k_i)y_(k_i)>=_(TemplateBox[{n}, SemidefiniteConeList])0
    where
  • The variable specification vars should be a list with elements giving variables in one of the following forms:
  • vvariable with name and dimensions inferred
    vRealsreal scalar variable
    vIntegersinteger scalar variable
    vvector variable restricted to the geometric region
    vVectors[n,dom]vector variable in or
    vMatrices[{m,n},dom]matrix variable in or
  • The constraints cons can be specified by:
  • LessEqualscalar inequality
    GreaterEqualscalar inequality
    VectorLessEqualvector inequality
    VectorGreaterEqualvector inequality
    Equalscalar or vector equality
    Elementconvex domain or region element
  • With SemidefiniteOptimization[f,cons,vars], parameter equations of the form parval, where par is not in vars and val is numerical or an array with numerical values, may be included in the constraints to define parameters used in f or cons. »
  • The primal minimization problem 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 semidefinite optimization has a dual: »
  • maximize-TemplateBox[{Tr, paclet:ref/Tr}, RefLink, BaseStyle -> {2ColumnTableMod}][a_0.lambda]
    subject to constraintsTemplateBox[{Tr, paclet:ref/Tr}, RefLink, BaseStyle -> {2ColumnTableMod}][a_j.lambda]=c_j,j=1,..., k,lambda>=_(TemplateBox[{n}, SemidefiniteConeList])0
    where
  • The possible solution properties "prop" include:
  • "PrimalMinimizer"a list of variable values that minimizes the objective function
    "PrimalMinimizerRules"values for the variables vars={v1,} that minimize
    "PrimalMinimizerVector"the vector that minimizes
    "PrimalMinimumValue"the primal minimum value
    "DualMaximizer"the matrix that maximizes -TemplateBox[{Tr, paclet:ref/Tr}, RefLink, BaseStyle -> {3ColumnTableMod}][a_0.lambda]
    "DualMaximumValue"-TemplateBox[{Tr, paclet:ref/Tr}, RefLink, BaseStyle -> {3ColumnTableMod}][a_0.lambda^*]the dual maximum value
    "DualityGap"-TemplateBox[{Tr, paclet:ref/Tr}, RefLink, BaseStyle -> {3ColumnTableMod}][a_0.lambda^*] - c.x^*the difference between the dual and primal optimal values
    "Slack"matrix that converts inequality constraints to equality
    "ConstraintSensitivity"
    sensitivity of to constraint perturbations
    "ObjectiveVector"the linear objective vector
    "ConstraintMatrices"the list of constraint matrices
    {"prop1","prop2",} several solution properties
  • The following options may be given:
  • MaxIterationsAutomaticmaximum number of iterations to use
    MethodAutomaticthe method to use
    PerformanceGoal$PerformanceGoalaspects of performance to try to optimize
    ToleranceAutomaticthe tolerance to use for internal comparisons
  • The option Method->method may be used to specify the method to use. Available methods include:
  • Automaticchoose the method automatically
    "CSDP"CSDP (COIN semidefinite programming) library
    "DSDP"DSDP (semidefinite programming) library
    "SCS"SCS (splitting conic solver) library
  • Computations are limited to MachinePrecision.

Examples

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Basic Examples  (3)

Minimize subject to the linear matrix inequality constraint  a_(0)+a_(1)x_1+a_(2) x_2_(TemplateBox[{2}, SemidefiniteConeList])0:

The optimal point is where is smallest within the region defined by the constraints:

Minimize subject to the linear matrix inequality constraint  a_(0)+a_(1)x_1+a_(2) x_2_(TemplateBox[{2}, SemidefiniteConeList])0:

Use the equivalent formulation with the objective vector and constraint matrices:

Minimize subject to  a_(0)+a_(1)x_1+a_(2) x_2_(TemplateBox[{2}, SemidefiniteConeList])0,x_1 in Z,x_2 in R:

Use the equivalent formulation with the objective vector and constraint matrices:

Scope  (27)

Basic Uses  (17)

Minimize subject to constraints :

Minimize when the matrix is positive semidefinite:

Find the solution:

Minimize subject to the linear matrix inequality constraint a_1 x+a_2 y+a_0_(TemplateBox[{2}, SemidefiniteConeList])0:

The left-hand side of the constraint can be given in evaluated form:

Express the problem with the objective vector and constraint matrices:

Use a vector variable :

Use a vector variable and Inactive[Plus] to avoid unintended threading:

Use a vector variable and parameter equations to avoid unintended threading:

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

Use Vectors[n] to specify the dimension of a vector variable when it is ambiguous:

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 and vector inequality:

Specify non-negative constraints using NonNegativeReals ():

An equivalent form using vector inequalities:

Second-order cone constraints of the form TemplateBox[{{{a, ., x}, +, b}}, Norm]<=alpha.x+beta can be used:

"NormCone" constraints of the form a.x+b_(TemplateBox[{}, NormConeString])0 can be used:

Specify integer domain constraints using Integers:

Specify integer domain constraints on vector variables using Vectors[n,Integers]:

Specify non-negative integer domain constraints using NonNegativeIntegers ():

Specify non-positive integer domain constraints using NonPositiveIntegers ():

Primal Model Properties  (3)

Minimize the function subject to the constraint (x 1; 1 y)_(TemplateBox[{2}, SemidefiniteConeList])0:

Get the primal minimizer as a vector:

Get the minimal value:

Extract the objective vector:

Extract the constraint matrices:

Use the extracted objective vector and constraint matrices for direct input:

The slack for an inequality g(x)_(TemplateBox[{n}, SemidefiniteConeList])0 at the minimizer is given by :

Extract the minimizer and constraint matrices:

Verify that the slack matrix satisfies :

Dual Model Properties  (3)

Minimize subject to :

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:

Construct the dual problem using constraint matrices extracted from the primal problem:

Extract the objective vector and constraint matrices:

The dual problem is to maximize subject to :

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

The "DualMaximumValue" is:

The "DualMaximizer" can be obtained with:

Sensitivity Properties  (4)

Use "ConstraintSensitivity" to find the change in optimal value due to constraint perturbations:

The sensitivity is a matrix:

Consider new constraints where is the perturbation:

The approximate new optimal value is:

Compare to directly solving the perturbed problem:

The optimal value changes according to the signs of the sensitivity matrix elements:

At negative sensitivity element position, a positive perturbation will decrease the optimal value:

At positive sensitivity element position, a positive perturbation will increase the optimal value:

Express the perturbed constraints symbolically using Sylvester's criterion for semidefiniteness:

With this form, the minimum value can be found exactly as a function of the parameters around 0:

Make a symmetric matrix with the derivatives of the minimum with respect to the parameters:

This is the sensitivity to perturbation given by the "ConstraintSensitivity" property:

The constraint sensitivity can also be obtained as the negative of the dual maximizer:

Options  (8)

Method  (5)

The default method "CSDP" is an interior point method:

"DSDP" is an alternative interior point method:

"SCS" uses a splitting conic solver method:

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

Compute exact and approximate solutions:

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

"SCS" has a default tolerance of :

When the default method "CSDP" produces a message, try "DSDP" first:

In this case, "DSDP" succeeds in finding a good solution:

"SCS" with a default tolerance of 10-3 is an alternative method to try:

The quality of the result with "SCS" can often be improved with a smaller Tolerance:

PerformanceGoal  (1)

The default value of the option PerformanceGoal is $PerformanceGoal:

Use PerformanceGoal"Quality" to get a more accurate result:

Use PerformanceGoal"Speed" to get a result faster, but at the cost of quality:

Compare the timings:

The "Speed" goal gives a less accurate result:

Tolerance  (2)

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 result, but may take longer to compute:

A smaller tolerance takes longer:

The tighter tolerance gives a more precise answer:

Applications  (33)

Basic Modeling Transformations  (13)

Maximize subject to  (-x 1; 1 -y)_(TemplateBox[{2}, SemidefiniteConeList])0. Solve a maximization problem by negating the objective function:

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

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

The largest eigenvalue satisfies lambda I-A(x,y)>=_(TemplateBox[{2}, SemidefiniteConeList])0. The largest by absolute value negative eigenvalue of is the largest eigenvalue of and satisfies lambda I+A(x,y)>=_(TemplateBox[{2}, SemidefiniteConeList])0:

Find that minimizes the largest singular value of a linear in matrix :

The largest singular value of is the square root of the largest eigenvalue of and from a preceding example it satisfies or equivalently (sigma I A(x,y); A(x,y)^T sigma I)_(TemplateBox[{5}, SemidefiniteConeList])0:

Plot the result:

Minimize . Using an auxiliary variable , transform the problem to minimize such that . This is the same as :

A Schur complement condition says that if , a block matrix iff . Thus for and for , since then must be 0:

Use the constraint directly and it will automatically convert into semidefinite form:

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

Check that implies :

Using the Schur complement condition, iff (t alpha.x+beta; alpha.x+beta gamma.x+delta )_(TemplateBox[{2}, SemidefiniteConeList])0. Use Inactive[Plus] for constructing the constraints to avoid threading:

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 :

Since λ0, :

Minimize subject to (x 1; 1 y)_(TemplateBox[{2}, SemidefiniteConeList])0. Convert the objective into a linear function with the additional constraint , which is equivalent to :

Minimize subject to (x 1; 1 y)_(TemplateBox[{2}, SemidefiniteConeList])0. Convert the objective into a linear function using and the additional constraints :

Minimize subject to (x 1; 1 y)_(TemplateBox[{2}, SemidefiniteConeList])0. Convert the objective into a linear function with the additional constraint , which is equivalent to :

Minimize subject to , where is a nondecreasing function, by instead minimizing . The primal minimizer will remain the same for both problems. Consider minimizing , subject to  (x 1; 1 y)_(TemplateBox[{2}, SemidefiniteConeList])0:

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

Data-Fitting Problems  (5)

Find the coefficients of a fifth-order polynomial by minimizing TemplateBox[{{{a, ., c}, -, b}}, Norm] that fits a discrete dataset:

Select the polynomial bases and construct the input matrix using DesignMatrix and output vector:

Using an auxiliary variable , the objective is transformed to minimize such that , which is equivalent to as shown under Basic Modeling Transformations:

Compare fit with data:

Find an approximating function to discrete data that varies on a logarithmic scale by minimizing TemplateBox[{{{a, ., x}, -, b}, infty}, Norm2]=max_i|a_i.x-b| using Chebyshev bases:

Select Chebyshev basis functions and compute their values at the random data points:

Since the data is on a logarithmic scale, direct data-fitting is not ideal. Instead, transform the problem to minimize . Using auxiliary variable , minimize such that . This constraint is equivalent to :

Find the coefficients of the approximating function:

The resulting fit is:

Visualize the fit:

The data-fitting can also be obtained directly using the function Fit. However, without the log transformation, there are significant oscillations in the approximating function:

Represent a given bivariate polynomial in terms of 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:

Cardinality constrained least squares: minimize such that has at most nonzero elements:

Let be a decision vector such that if , then is nonzero. The decision constraints are:

To model constraint when , chose a large constant such that :

Using an auxiliary variable , the objective is transformed to minimize such that , which is equivalent to :

Solve the cardinality constrained least-squares problem:

The subset selection can also be done more efficiently with Fit using regularization. First, find the range of regularization parameters that uses at most basis functions:

Find the nonzero terms in the regularized fit:

Find the fit with just these basis terms:

Find the best subset of functions from a candidate set of functions to approximate given data:

The approximating function will be :

A maximum of 5 basis functions are to be used in the final approximation:

The coefficients associated with functions that are not chosen must be zero:

Find the best subset of functions:

Compare the resulting approximating with the given data:

Geometry Problems  (6)

Find the smallest disk centered at of radius that encloses a set of points:

For each point , the constraint TemplateBox[{{c, -, {p, _, i}}}, Norm]<=r, i=1,2,...,n must be satisfied. This constraint is equivalent to . Use Inactive when forming the constraints:

Find the enclosing disk by minimizing the radius :

Visualize the enclosing region:

The minimal area bounding disk can also be found using BoundingRegion:

Find the smallest ellipse parametrized as {x:TemplateBox[{{{a, ., x}, +, b}}, Norm]<=1} that encompasses a set of points by minimizing the area:

For each point , the constraint TemplateBox[{{{a, ., {p, _, i}}, +, b,  }}, Norm]<=1, i=1,2,...,n must be satisfied:

The area is proportional to . Applying the monotone function Log, the function to minimize is . This in turn is equivalent to minimizing :

Convert the parameterized ellipse into the explicit form :

A bounding ellipse, not necessarily minimal area, can be found using BoundingRegion:

The optimal ellipse has a smaller area:

Find the smallest ellipsoid parametrized as {x:TemplateBox[{{{a, ., x}, +, b}}, Norm]<=1} that encompasses a set of points in 3D by minimizing the volume:

For each point , the constraint TemplateBox[{{{a, ., {p, _, i}}, +, b,  }}, Norm]<=1, i=1,2,...,n must be satisfied:

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

Convert the parameterized ellipse into the explicit form :

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

Find the maximum area ellipse parametrized as {x=C.y+d :TemplateBox[{y}, Norm]<=1} that can be fitted into a convex polygon:

Each segment of the convex polygon can be represented as intersections of half-planes :

Applying the parametrization to the half-planes gives sup{(a_i.C).y:TemplateBox[{y}, Norm]<=1}+a_i.d<=b_i. The term sup{(a_i.C).y:TemplateBox[{y}, Norm]<=1}=sup{TemplateBox[{{{a, _, i}, ., C}}, Norm]TemplateBox[{y}, Norm]Cos(theta):TemplateBox[{y}, Norm]<=1}=TemplateBox[{{{a, _, i}, ., C}}, Norm]. Thus, the constraints are  TemplateBox[{{{a, _, i}, ., C}}, Norm]+a_i.d<= b_i, which is equivalent to :

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

Convert the parameterized ellipse into the explicit form as :

Find the center and radius of a disk given by that encloses three ellipses of the form :

Using S-procedure, it can be shown that the disk contains the ellipses iff :

The goal is to minimize the radius given by . Using auxiliary variable , the objective is to minimize such that , which can be written as :

Find the center and radius of the disk:

The disk is given by:

Convert the quadratic form of the ellipse to the explicit form :

Visualize the result:

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

Using 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:

Classification Problems  (2)

Find an ellipse that separates two groups of points and :

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

Find the coefficients of the separating ellipsoid:

Visualize the result:

Find an ellipse that is as close as possible to a circle that separates two groups of points and :

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

For the ellipsoid to be as close as possible to a circle, the constraint :

Find the coefficients of the separating ellipsoid by minimizing :

Visualize the result:

Graph Problems  (3)

The Lovász number, computable using semidefinite optimization, is used as a bound for hard compute graph invariants:

The Lovász number is an upper bound for the Shannon capacity of a graph:

According to the Lovász sandwich theorem:

The Lovász number for a graph is given by , where , and for . It can be written in a dual semidefinite form: , , subject to and , and 0 elsewhere:

Compare to the exact Lovász number values from GraphData:

Find the approximate result for when the exact result is not available:

Find for a random graph:

The max-cut problem determines a subset of the vertices of a graph, for which the sum of the weights of the edges that cross from to its complement is maximized. Let for and for . Maximize , where and is the Laplacian matrix of the graph:

For smaller cases, the max-cut problem can be solved exactly, but this is impractical for larger graphs since in general the problem has NP-complete complexity:

The problem minimizes , where Y=x.TemplateBox[{x}, Transpose] is a symmetric rank-1 positive semidefinite matrix, with for each , equivalent to , where is the matrix with 1 at the diagonal position and 0 everywhere else. To make the solution practical, solve a relaxed problem where the rank-1 condition is eliminated. For such , a cut is constructed by randomized rounding: decompose Y=U TemplateBox[{U}, Transpose], let be a uniformly distributed random vector of the unit norm and let . For demonstration, a function is defined that shows the relaxed value, the rounded value and the graph with the vertices in shown as red:

Find an approximate max cut using the previously shown procedure, and compare with the exact result:

Find the max cut for a grid graph:

Find the max cut for a random graph:

Compare timings for the relaxed and exact algorithms for a Peterson graph:

Find a subset of specified graph with vertices such that the sum of the weights of the edges that cross from to its complement is maximized. Specify the graph:

The objective is to maximize , where Y=x.TemplateBox[{x}, Transpose] is a symmetric rank-1 positive semidefinite matrix and is the Laplacian matrix of the graph:

Let for and for ; then and :

Drop the rank-1 matrix assumption and solve the resulting max-cut problem:

Extract the subsets and :

Display the subsets on the graph:

Control & Dynamic Systems Problems  (3)

Show that a linear dynamical system will be asymptotically stable for any initial condition. The system is said to be stable iff there exists a positive definite matrix such that where is called the Lyapunov function:

Differentiating the Lyapunov function gives . Therefore, the stability conditions are :

Find a matrix :

The eigenvalues of are all negative, making the matrix negative definite, which proves stability:

Since the analytic solution to the system is , numerically verify that any system will go to zero for any initial condition. Take for this simulation:

Find a controller , such that the closed-loop system is stable:

Using Lyapunov stability theorem, the objective is to find a matrices such that the stability constraints are satisfied. Letting , the first constraint becomes a proper semidefinite constraint :

Find the matrices :

The control matrix can be computed as :

The closed-loop system is stable if the real parts of the eigenvalues of are negative:

Perform a numerical run to see that the system is stable:

Find a Lyapunov function of the dynamical system :

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

Construct the matrix :

For stability, :

Match the coefficients such that they are all positive for and negative for :

Find the matrix :

The Lyapunov function is given by:

Visualize the Lyapunov function. The minimal location of the function matches with the location of the attractor:

Structural Optimization Problems  (1)

Design a minimum-weight truss that is anchored on one end of the wall and must withstand a load on the other end:

The truss can be modeled using links and nodes. Each node is connected to a neighboring node by a link. Specify the node positions :

Specify the nodes that are anchored to the wall:

Specify the node at which the load is applied:

Specify the nodes that are connected to each other through a link and compute the length of each link:

Visualize the unoptimized truss:

The links are circular bars. Each link must be formed from one out of a group of bars of cross sections . Let be a decision vector for each link , such that if , then bar is selected. For link , the area is then defined as . The objective is to minimize the weight:

The bar selection constraint is:

Only one bar must be selected for each link. The binary constraint is:

Find the indices of the nodes that are not anchored:

The stiffness matrix of the system is given by , where is the total number of nodes, is the number of nodes that are anchored and is the set of all the links. The vector if ; if , else 0:

Let be the force vector for the entire system. At each of the nodes that is not anchored, the force is . The node where force is applied is :

Let be the maximum allowable deflection at any node. Let be displacement of node ; then is satisfied if , where is the stiffness matrix associated with link :

Collect all the variables:

Find the optimal structure of the truss:

Extract the links that are part of the optimal truss:

Visualize the optimal truss. The links that are part of the optimal truss are colored-coded based on the rod area being used:

Properties & Relations  (8)

SemidefiniteOptimization gives the global minimum of the objective function:

Plot the objective function with the minimum value over the feasible region:

Minimize gives global exact results for semidefinite problems:

Compare to SemidefiniteOptimization:

NMinimize can be used to get inexact results using global methods:

FindMinimum can be used to obtain inexact results using local methods:

ConicOptimization is more general than SemidefiniteOptimization:

SecondOrderConeOptimization is a special case of SemidefiniteOptimization:

QuadraticOptimization is a special case of SemidefiniteOptimization:

Use auxiliary variable and minimize with additional constraint :

LinearOptimization is a special case of SemidefiniteOptimization:

Possible Issues  (4)

The constraints at the optimal point are satisfied up to some tolerance:

With default options, the constraint violation tolerance is 10-8:

The minimum value of an empty set or infeasible problem is defined to be :

The minimizer is Indeterminate:

The minimum value for an unbounded set or unbounded problem is :

The minimizer is Indeterminate:

Dual related solution properties for mixed-integer problems may not be available:

Introduced in 2019
 (12.0)
 |
Updated in 2020
 (12.1)