1%*****************************************************************************
2% DSDP5:  Dual-Scaling Algorithm for Positive Semidefinite Programming
3% Copyright (c) 2004 by
4% S. J. Benson, Y. Ye
5% Last modified: 20 August 2004
6%*****************************************************************************
7%
8% > [STAT,y] = DSDP() returns a structure STAT with relevant information
9%              concerning the performance of the solver and an approximate
10%              dual solution y .
11%
12%   The fields of STAT are:
13%
14%   Objective Value:
15%       stype   = 'PDFeasible' if an feasible primal and dual solutions
16%                were computed, 'Infeasible' if dual
17%                infeasibility was detected, and 'Unbounded' if primal
18%                infeasibility is detected.
19%       obj    = objective value at solution
20%       pobj   = an approximately optimal objective value to (P)
21%       dobj   = an approximately optimal objective value to (D)
22%       stopcode = 0: convergence to prescribed accuracy,
23%                 ~0: termination for other reasons
24%
25%   Characteristics of Solution
26%       tracex = if X was returned, this is the trace of it.  This number
27%                also corresponds to the minimum penalty parameter that
28%                could solve this problem.  IMPORTANT: For improved
29%                performance, consider using penalty parameter (see DOPTIONS)
30%                other than the default.
31%       penalty = the penalty parameter used by the solver, which must be
32%                greater than the trace of the primal solution (see above).
33%       errors = several error estimates to the solution. (See DERROR)
34%       ynorm = the largest element of y (infinity norm).
35%       boundy = the bounds placed on the magnitude of each variable y.
36%       mu = final barrier parameter.
37%       r      = the multiple of the identity matrix added to
38%                C-A'(y) in the final solution to make S positive definite.
39%                That is, S = C - A'y + r*I.
40%       xy, xdy, xmu = values used to compute X.
41%
42%   Solver Statistics
43%       iterates = number of iterations used by the algorithm.
44%       pstep = final primal step size.
45%       dstep = final dual step size.
46%       pnorm = final norm of distance to central path.
47%       gaphist = a history of the duality gap.
48%       infhist = a history of the dual infeasibility.
49%       datanorm = the Frobenius norm of C, A and b,
50%
51%  See also: DSDP
52%
53% DSDP5
54% Copyright (c) 2004 by
55% S. Benson and Y. Ye
56% Last modified: October 2004
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