Question

在pyomo中，我有一个通过pyomo.environ.Piecewise定义的分段线性约束。我不断收到类似的警告

Piecewise component '<component name>' has detected slopes of consecutive piecewise segments to be within <tolerance> of one another. Refer to the Piecewise help documentation for information on how to disable this warning.

我知道我可以提高公差并摆脱警告，但是我想知道是否存在一种通用方法（通过Pyomo或numpy）来减少“段”的数量（如果两个连续的斜率都低于给定值）容忍。

我显然可以自己实现，但是我想避免重新发明轮子。

Answer 1

好的，这就是我的想法。绝对没有针对性能进行优化，但是我的情况取决于几点。它还缺少对输入的更多验证（例如def reduce_piecewise(x, y, abs_tol): """ Remove unnecessary points from piece-wise curve. Points are remove if the slopes of consecutive segments differ by less than `abs_tol`. x points must be sorted and unique. Consecutive y points can be the same though! Parameters ---------- x : List[float] Points along x-axis. y : List[float] abs_tol : float Tolerance between consecutive segments. Returns ------- (np.array, np.array) x and y points - reduced. """ if not len(x) == len(y): raise ValueError("x and y must have same shape") x_reduced = [x[0]] y_reduced = [y[0]] for i in range(1, len(x) - 1): left_slope = (y[i] - y_reduced[-1])/(x[i] - x_reduced[-1]) right_slope = (y[i+1] - y[i])/(x[i+1] - x[i]) if abs(right_slope - left_slope) > abs_tol: x_reduced.append(x[i]) y_reduced.append(y[i]) x_reduced.append(x[-1]) y_reduced.append(y[-1]) return np.array(x_reduced), np.array(y_reduced)被排序且唯一）。

>>> x = np.array([0, 1, 2, 3])
>>> y = np.array([0, 1, 2, 3])
>>> reduce_piecewise(x, y, 0.01)
(array([0, 3]), array([0, 3]))

这是一些示例：

>>> x = np.array([0, 1, 2, 3, 4, 5])
>>> y = np.array([0, 2, -1, 3, 4.001, 5]) # 4.001 should be removed
>>> reduce_piecewise(x, y, 0.01)
(array([0, 1, 2, 3, 5]), array([ 0.,  2., -1.,  3.,  5.]))

data_path

自动减少分段功能组件-Pyomo

1 个答案: