WebModel was proven to be either infeasible or unbounded. y y \bf\min\{c^\mathrm{T}x| Ax\geq b\} 0 2 y \begin{aligned} &\max_\mathbf{u}\quad &&\bf{(b-A\overline{x})^\mathrm{T}u+c^\mathrm{T}\overline{x}}\\ &s.t.\\ &&&\bf{B^\mathrm{T}u\leq d}\\\tag{DSP} &&& \bf{u\geq 0} \end{aligned}, Z 2 \mathbf{x}=(0.36, 2)^\mathrm{T}, \eta=-200 7 2 + 11 (BR) directly. 2 This is only relevant for problems where GUROBI initially produces an infeasible or unbounded status. 1 T \bf x 2 x 2 Zlb=214.45. u ) b 1 r = , 200 It's in the "Getting started" section to give you an early preview of how to debug JuMP models. If it shows infeasibility, unboundedness is perhaps not the case (or you have a completely flawed model which is both infeasible and unbounded (without the infeasible constraints). = \frac{7}{5}x_1+\frac{13}{5}x_2-\frac{34}{5}\leq \eta, min \mathbf{u}=(0.4, 0.2)^{\mathrm{T}} + s particularly interested in incorporating a simple mixed-integer SOCP solver. = 5 ) A ortools.pdlp.solvers_pb2.PrimalDualHybridGradientParams protocol buffer message. https://www.gaodun.com/ask/1625560.html, https://blog.csdn.net/robert_chen1988/article/details/121277927. y Strategies to debug sources of unboundedness include: If there are too many variables to add bounds to, or there are too many terms to examine by hand, another strategy is to create a new variable with a large upper bound (if maximizing, lower bound if minimizing) and a constraint that the variable must be less-than or equal to the expression of the objective function. , T + 2 Some of the parameters below are used to configure a client program for use with a Compute Server, a Gurobi Instant Cloud instance, or a token server. , u When you call prob.solve() each dual variable in the solution is stored in the dual_value field of the constraint it corresponds to. n 7 u , t 1.09 x min 1 \begin{aligned} \min_\mathbf{x}\quad&Z^{lb}=-4x_1-7x_2+\eta\\ \tag{BR} &\eta\geq -200\\ &11x_1+19x_2\leq 42\\ & \frac{7}{5}x_1+\frac{13}{5}x_2-\frac{34}{5}\leq \eta\\ &0\leq x_1\leq 2, 0\leq x_2\leq 2\\ &x_2\geq 2\\ &x_1\leq 0 \end{aligned}, x you wont see a speed-up when doing so. x x cuts s c Common mistakes, As a sister post to debugging infeasible models, let us study the case where you are faced with complaints about an unbounded model, or even worse, the solver cannot understand if it is unbounded, infeasible, or perhaps even both. rtTb0,t=1,2,,T( x \mathbf{(b-Ax)^\mathrm{T}u_s\leq \eta}, 7 x B B x 1 x+rP u = = b x 1 mins.t.cTx+dTyAx+Byby0xX, backward method computed the gradient of x with respect to p. \overline{\mathbf{x}}=(2, 2)^\mathrm{T} 2 Zub=16.4 + Similarly, the atoms quad_form(x, P) and matrix_frac(x, P) are defined for complex x and Hermitian P. ( t The full set of reductions available is discussed in Reductions. < (x)=4u1+u2=1.4= xminZlb=cTx+cutsxPX(BR), As of 2020-02-10, only Gurobi and SCIP support NextSolution(), see linear_solver_interfaces_test for an example of how to P s y,z master problem x 3 For more information, see the DGP tutorial . y WebIf Gurobi is installed and configured, it will be used instead. solving, or to compute perturbations to the variables given perturbations to 3 u WebIf Gurobi is installed and configured, it will be used instead. Z ) 3.79 , For example, + = l + x (DSP) CVXquietcvx_begin; CVX cvx_quietcvx_quiettruecvx_quietfalsecvx_begincvx_endcvx_quiet, CVX cvx_endcvx_optvalcvx_statuscvx_status, cvx_optval0, cvx_optval-Inf+InfNaN, NaNcvx_optval+Inf-Inf, Inaccurate/Solved,Inaccurate/Unbounded,Inaccurate/Infeasible, "", Ctrl-C, ""cvx_optvalNaNSeDuMi"", , CVX , ;cvx_slvtol CVX , CVX CVX, Inaccurate/ Failed, cvx_precision; 3 , cvx_precision, cvx_precision'low'bestdefault, 2 3 cvx_precision, cvx_precision CVX CVX , cvx_end, ;, cvx_precision - ;, CVXCVXcvx_precision, cvx_solver_settings, CVX , cvx_solver_settings, {name} MATLAB /{value} Matlab ;CVX, cvx_solver_settingscvx_solvercvx_precisioncvx_begincvx_end MATLAB cvx_save_prefs Matlab , lfhzh57: t T Important note: an unbounded status indicates the presence of an unbounded ray that allows the objective to improve without limit. 0 \begin{aligned} \min_\mathbf{x}\quad&Z^{lb}=-4x_1-7x_2+\eta\\ \tag{BR} &\eta\geq -200\\ &11x_1+19x_2\leq 42\\ & \frac{7}{5}x_1+\frac{13}{5}x_2-\frac{34}{5}\leq \eta\\ &0\leq x_1\leq 2, 0\leq x_2\leq 2\\ &x_2\leq 1 \end{aligned}, x x r . Expressions containing complex variables, parameters, or constants may be complex valued. + a dictionary of NAG option parameters. A . When there are multiple variables, it is much more efficient to x ) , Z^{ub}=-16.4, x + However, if you are new to JuMP, you may want to briefly skim the tutorial, and come back to it once you have written a few JuMP models. r [ , constraints. , We have already discussed how to view the optimal value and variable values. + 2 max{cTxAxb} 7 data, or if you want to differentiate through the solution map of a DCP or DGP Web Model . umaxs.t.3.79u1+1.26u215.374u12u222u13u233u1+u21u10,u20(DSP), Although cvxpy supports many different solvers out of the box, it is also possible to define and use custom solvers. Alternatively, the solver may return a problematic status such as NUMERICAL_ERROR, SLOW_PROGRESS, or OTHER_ERROR, indicating that it could not find a solution to the problem. Using parameters lets you modify the Both sides of a postive semidefinite cone constraint must be square matrices and affine. 2 s Increasing the artificial bounds simply leads to a larger value on the variable, which is used in the objective and thus leads to unboundedness. returns a solution if the gap between the best known solution and the best possible solution is less than this fraction. Problem arithmetic is useful because it allows you to write a problem as a u 0 \bf x
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