在cvxpy中,DCP规则确保所构建的问题满足凸性要求,从而可以使用内点法等凸优化算法来求解。遵循DCP规则的问题被称为DCP合规问题。 2. 列举可能导致“problem does not follow DCP rules”错误的常见原因 非线性表达式不符合DCP规则:例如,使用了不满足DCP规则的函数(如某些非线性函数)或者对这些函数进行了不当的组合。
Hi, everyone, this problem bothers me for a long time u = cp.Variable((2, 3)) obj = cp.sum(u@u.T) however, the expression of obj is Expression(UNKNOWN, UNKNOWN, ()). But the expression of the obj should be convex, so how should I deal wi...
To Reproduce import cvxpy as cp x = cp.Variable(1) cp.Problem(cp.Minimize(x),[cp.exp(x)-x>=0]).solve() Output cvxpy.error.DCPError: Problem does not follow DCP rules. Specifically: The following constraints are not DCP: 0.0 <= exp(var1) ...
DCPError: Problem does not follow DCP rules. Specifically: The objective is not DCP. Its following subexpressions are not: ... And then an error I don't undeerstand haha. python cvxpy convex-optimization Share Improve this question asked...
组合优化是量化投资策略实施过程中非常重要的步骤，组合优化的过程是结合不同的投资目标及风险约束给出最...
应该使用cvxpy函数quad_over_lin(y,x)来表示y^2/x。
。在综合和实现的各个子步骤（opt_design、place_design和route_design）之后都会生成相应的.dcp文件。
(): DCPError: Problem doesnotfollow DCP rules. Specifically: The following constraints arenotDCP: power(var58 + -[[5.39791975e-03] [1.26209297e-03] [8.00351893e-05] [5.26548876e-02] [7.59655721e-03] [2.57535232e-03] [5.03372951e-04] [1.47480336e-03] [1.38599909e-04] [5.74108704e-03]...
cvxpy是一个求解凸优化的python库,几乎所有的线性规划,二次规划和几何规划问题都只需要你写出问题形式和约束条件即可直接求解,非常简单高效。 软间隔SVM的对偶问题是最优化问题: (SVMsoftdual)maxa∑i=1mai−12∑i=1m∑j=1maiajyiyjxi⊤xjs.t.∑i=1maiyi=0(Box Constraint)0≤ai≤C,i=1,⋯,m ...
"""Does the problem satisfy DCP rules? """ return all( expr.is_dcp() for expr in self.constraints + [self.objective]) @perf.compute_once def is_dgp(self): """Does the problem satisfy DGP rules? """ return all( expr.is_dgp() for expr in self.constraints + [self.objective]) ...