The solver

ClusteredLowRankSolver.jl implements a primal-dual interior-point method. That is, it solves both the primal and the dual problem. The problem given to the solver is considered to be in dual form. For more information on the primal-dual algorithm, see [2] and [3].

A Problem can be solved using the function solvesdp. This first converts the problem to a ClusteredLowRankSDP, after which it is solved using the algorithm.

	solvesdp(problem::Problem; kwargs...)
    solvesdp(sdp::ClusteredLowRankSDP; kwargs...)

Solve the semidefinite program generated from problem or sdp.

Keyword arguments:

  • prec (default: precision(BigFloat)): the precision used
  • gamma (default: 0.9): the step length reduction; a maximum step length of α reduces to a step length of max(gamma*α,1)
  • beta_(in)feasible (default: 0.1 (0.3)): the amount mu is tried to be reduced by in each iteration, for (in)feasible solutions
  • omega_p/d (default: 10^10): the starting matrix variable for the primal/dual is omega_p/d*I
  • maxiterations (default: 500): the maximum number of iterations
  • duality_gap_threshold (default: 10^-15): how near to optimal the solution needs to be
  • primal/dual_error_threshold (default:10^-30): how feasible the primal/dual solution needs to be
  • max_complementary_gap (default: 10^100): the maximum of dot(X,Y)/nrows(X) allowed
  • need_primal_feasible/need_dual_feasible (default: false): terminate when the solution is primal/dual feasible
  • verbose (default: true): print information after every iteration if true
  • step_length_threshold (default: 10^-7): the minimum step length allowed
  • primalsol (default: nothing): start from the solution (primalsol, dualsol) if both primalsol and dualsol are given
  • dualsol (default: nothing): start from the solution (primalsol, dualsol) if both primalsol and dualsol are given


Here we list the most important options. For the remaining options, see the documentation and the explanation of the algorithm in [3].

  • prec - The number of bits used for the calculations. The default is the BigFloat precision, which defaults to 256 bits.
  • duality_gap_threshold - Gives an indication of how close the solution is to the optimal solution. As a rule of thumb, a duality gap of $10^{-(k+1)}$ gives $k$ correct digits. Default: $10^{-15}$
  • gamma - The step length reduction; if a step of $\alpha$ is possible, a step of $\min(\gamma \alpha, 1)$ is taken. A lower gamma results in a more stable convergence, but can be significantly slower. Default: $0.9$.
  • omega_p, omega_d - The size of the initial primal respectively dual solution. A low omega can keep the solver from converging, but a high omega in general increases the number of iterations needed and thus also the solving time. Default: $10^{10}$
  • need_primal_feasible, need_dual_feasible - If true, terminate when a primal or dual feasible solution is found, respectively. Default: false.
  • primal_error_threshold, dual_error_threshold - The threshold below which the primal and dual error should be to be considered primal and dual feasible, respectively. Default: $10^{-15}$.


When the option verbose is true (default), the solver will output information for every iteration. In order of output, we have (where the values are from the start of the iteration except for the step lengths, which are only known at the end of the iteration)

  • The iteration number
  • The time since the start of the first iteration
  • The complementary gap $\mu = \langle X, Y \rangle / K$ where $K$ is the number of rows of $X$. Here $X$ and $Y$ denote the primal and dual solution matrices. The solution will converge to the optimum for $\mu \to 0$.
  • The primal objective
  • The dual objective
  • The relative duality gap
  • The primal matrix error
  • The primal scalar error
  • The dual (scalar) error
  • The primal step length
  • The dual step length
  • $\beta_c$. The solver tries to reduce $\mu$ by this factor in this iteration.

An example of the output of the Example from polynomial optimization is

iter  time(s)           μ       P-obj       D-obj        gap    P-error    p-error    d-error        α_p        α_d       beta
    1     11.9   1.000e+20   0.000e+00   0.000e+00   0.00e+00   1.00e+10   1.00e+00   1.95e+10   7.42e-01   7.10e-01   3.00e-01
    2     13.4   3.995e+19   1.999e+11  -2.907e+09   1.03e+00   2.58e+09   2.58e-01   5.65e+09   7.46e-01   7.17e-01   3.00e-01
    3     13.4   1.576e+19   3.079e+11  -4.779e+09   1.03e+00   6.53e+08   6.53e-02   1.60e+09   7.32e-01   7.31e-01   3.00e-01
   55     13.9   5.066e-14  -2.113e+00  -2.113e+00   8.39e-14   8.64e-78   2.59e-77   8.21e-73   1.00e+00   1.00e+00   1.00e-01
   56     13.9   5.067e-15  -2.113e+00  -2.113e+00   8.39e-15   8.64e-78   8.64e-78   8.39e-73   1.00e+00   1.00e+00   1.00e-01
Optimal solution found
 13.860834 seconds (13.74 M allocations: 913.073 MiB, 7.72% gc time, 93.09% compilation time)
 iter  time(s)           μ       P-obj       D-obj        gap    P-error    p-error    d-error        α_p        α_d       beta

Primal objective:-2.112913881423601867325289796075301826150007716044362101360781221096092533872562
Dual objective:-2.112913881423605414349991239275382883067580432169230529548206052006356176913883
Duality gap:8.393680245626824434313082297089851809408852609517159688543365552836941907249006e-16

Note that the first iteration takes long because the functions used by the solver get compiled. The function solvesdp returns the status of the solutions, the primal and dual solutions, the solve time and an error code (see below).


When the algorithm finishes due to one of the termination criteria, the status, the final solution together with the objectives, the used time and an error code is returned. The status can be one of

  • Optimal
  • NearOptimal - The solution is primal and dual feasible, and the duality gap is small ($<10^{-8}$), although not smaller than duality_gap_threshold.
  • Feasible
  • PrimalFeasible or DualFeasible
  • NotConverged


Although unwanted, errors can be part of the output as well. The error codes give an indication what a possible solution could be to avoid the errors.

  1. No error
  2. An arbitrary error. This can be an internal error such as a decomposition that was unsuccessful. If this occurs in the first iteration, it is a strong indication that the constraints are linearly dependent, e.g. due to using a set of sample points which is not minimal unisolvent for the basis used. Otherwise increasing the precision may help. This also includes errors which are due to external factors such as a keyboard interrupt.
  3. The maximum number of iterations has been exceeded. Reasons include: slow convergence, a difficult problem. Possible solutions: increase the maximum number of iterations, increase gamma (if gamma is small), change the starting solution (omega_p and omega_d).
  4. The maximum complementary gap ($\mu$) has been exceeded. Usually this indicates (primal and/or dual) infeasibility.
  5. The step length is below the step length threshold. This indicates precision errors or a difficult problem. This may be solved by increasing the initial solutions (omega_p and omega_d), or by decreasing the step length reduction gamma, or by increasing the precision prec. If additionally the complementary gap is increasing, it might indicate (primal and/or dual) infeasibility.


The solver supports multithreading. This can be used by starting julia with

julia -t n

where n denotes the number of threads.


On Windows, using multiple threads can lead to errors when using multiple clusters and free variables. This is probably related to Arb or the Julia interface to Arb.