hi,
for all who are interested:
take a look at the puny benchmark of LP solvers available in my GSoC openopt module (provided CVXOPT with glpk and mosek is installed, as well as lp_solve).
I shall try to connect the one to scikits during the next week (and switch for some weeks to other work, assigned to my GSoC milestones).
1st number is time elapsed (seconds), 2nd - cputime (opeopt bindings take less than 1-2% of the time)

LP name nVars nConstr density cvxopt_lp cvxopt_glpk cvxopt_mosek LPSolve LGPL GPL2 non-free LGPL trig 500 500 1 10.1/9.7 1.1/1.1 N/A(2) 0.4/0.4 trig 500 1000 1 16/15.9 3.5/3.4 N/A(2) 0.76/0.72 trig 1000 1000 1 99/98 6.8/6.7 N/A(2) 1.67/1.64 trig 1500 3000 1 479/470 177(1)/83 N/A(2) 7.9/7.7 trig_sparse 500 500 0.1 0.39/0.38 0.39/0.38 N/A(2) 0.16/0.17 trig_sparse 500 1000 0.1 4.83/4.64 0.83/0.82 N/A(2) 0.34/0.3 trig_sparse 1000 1000 0.1 12.7/12.5 1.88/1.86 N/A(2) 0.64/0.62 trig_sparse 1500 3000 0.1 131/128 17.5(1)/12.3 N/A(2) 3.91/3.81 (1): swap encountered (I have 1 Gb) (2): internal mosek error (maybe problems with my AMD Athlon 64 3800+ X2) you should also take into account lb bounds used, it gives additional nVars constraints. some benchmarks of glpk are also available at http://plato.asu.edu/ftp/lpfree.html mosek is available at http://plato.asu.edu/ftp/lpcom.html all the solvers are available via single interface, let me paste data from the LP() doc (excuse my English):     LP: constructor for Linear Problem assignment     valid calls are:     p = LP(f, <params>)     p = LP(f=objFunVector, <params>)     p = LP(f, A=A, Aeq=Aeq, Awhole=Awhole, b=b, beq=beq, bwhole=bwhole, dwhole=dwhole, lb=lb, ub=ub)         NB! Constraints can be separated in many ways,     either AX <= b, Aeq X = beq (MATLAB-, CVXOPT- style),     or  Awhole X {< | = | >} bwhole  (glpk-, lp_solve- and many other software style),     or any mix of them         INPUTS:     f: size n x 1 vector     A: size m1 x n matrix, subjected to A * x <= b     Aeq: size m2 x n matrix, subjected to Aeq * x = beq     Awhole: size m3 x n matrix, subjected to Awhole * x { < | = | > } bwhole     b, beq, bwhole: corresponding vectors with lengthes m1, m2, m3     dwhole: vector of length m3 from {-1,0,1}, descriptor, sign of what (Awhole*x_opt - bwhole) should be equal to     (this will simplify translating from other languages to Python and reduce the amount of mistakes     as well as amount of additional code lines)         OUTPUT: OpenOpt LP class instance         Solving of LPs is performed via     r = p.solve(string_name_of_solver)     r.xf - desired solution (NaNs if a problem occured)     r.ff - objFun value (<f,x_opt>) (NaN if a problem occured)     (see also other fields, such as CPUTimeElapsed, TimeElapsed, etc)     Currently string_name_of_solver can be:     LPSolve (LGPL) - requires lpsolve + Python bindings installations (all mentioned is available in http://sourceforge.net/projects/lpsolve)     cvxopt_lp (GPL) - requires CVXOPT (http://abel.ee.ucla.edu/cvxopt)     cvxopt_glpk(GPL2) - requires CVXOPT(http://abel.ee.ucla.edu/cvxopt) & glpk (www.gnu.org/software/glpk)     cvxopt_mosek(commercial) - requires CVXOPT(http://abel.ee.ucla.edu/cvxopt) & mosek (www.mosek.com)     Example:     Let's concider the problem     15x1 + 8x2 + 80x3 -> min        (1)     subjected to     x1 + 2x2 + 3x3 <= 15              (2)          8x1 +  15x2 +  80x3 <= 80      (3)     8x1  + 80x2 + 15x3 <=150      (4)     100x1 +  10x2 + x3 >= 800     (5)     80x1 + 8x2 + 15x3 = 750         (6)     x1 + 10x2 + 100x3 = 80           (7)         Let's pass (2), (3) to A X <= b, (6) to Aeq X = beq     and rest of constraints will be handled via Awhole, bwhole, dwhole         array, list, matrix and real number are accepted:     f = array([15,8,80])     A = mat('1 2 3; 8 15 80')     b = [15, 80]     Aeq = mat('80 8 15')     beq = 750     Awhole = mat('8 80 15; 1 10 100; 100 10 1')     bwhole = array([150, 80, 800])     dwhole = [-1, 0, 1]     p = LP(f, A=A, Aeq=Aeq, Awhole=Awhole, b=b, beq=beq, bwhole=bwhole, dwhole=dwhole)     r = p.solve('LPSolve') #lp_solve must be installed     print 'objFunValue:', r.ff # should print 204.350288002     print 'x_opt:', r.xf There are also NLP, NSP classes available (currently unconstrained solvers ralg and ShorEllipsoid are supplied). LPSolve and glpk also provide MILP solvers, but cvxopt connection to glpk (that one is required) can't handle the integer indexes, so in my MILP class in nearest future  will be only one solver (LPSolve) I know that there is a software for connecting commercial LP/MILP/QP solver CPLEX to Python, but (at least currently)  I have no time for the one. I shall gladly take into account all your suggestions. Regards, Dmitrey.

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