A small example of Holt-Winters double exponential smoothing
in Python2 with NumPy and scipy.signal . This is a good bad example, in that yhat[n] == x[n], i.e. predict next == current (a = 1, b = 0) is ~ optimal.
|""" lpgen34.py LP testcase generator|
|d=3: 3n^2 x n^3, 3 1s in each column|
|n=55: A 9075 x 166375, 499125 nnz, glpsol simplex 100 minutes|
|d=4: 4n^3 x n^4, 4 1s in each column, n in each row|
|n=16: A 2^14 x 2^16, 2^18 nnz, glpsol simplex 10 hours|
|# keywords: linear programming, test case, generator, Latin-square|
|# https://math.stackexchange.com/questions/3370934/3d-permutation-matrices -- 4d too|
Keywords: linear programming, LP, GLPK, runtime, test case, Netlib
# 16 Aug 2019 08:34z ~bz/py/opt/lp/netlib Denis-iMac 10.10.5 Intel(R) Core(TM) i5-3330S CPU @ 2.70GHz Apple LLVM version 7.0.2 (clang-700.1.81) GLPSOL: GLPK LP/MIP Solver, v4.65 (uses only 1 core)
transp-linprog.py below is a 6 x 6 testcase of
from a GLPK example, transp. In outline:
for method in ["interior-point", "revised simplex", "simplex"]: for sparse in [True, False] try: linprog( ... )
Keywords: stochastic programming, risk, linear programming, python
Users of stochastic programming sometimes look only at "expected" payoff and ignore risk. For example, a well-known tutorial problem, Dakota furniture from Higle 2005, gives a max-expected profit $ 1730, but with 30 % chance of $ 650 loss, 70 % $2750 profit. That looks risky to me (an engineer, not a businessman).
|Intergrid: interpolate data given on an N-d rectangular grid|
|Purpose: interpolate data given on an N-dimensional rectangular grid,|
|uniform or non-uniform,|
|with the fast `scipy.ndimage.map_coordinates` .|
|Non-uniform grids are first uniformized with `numpy.interp` .|
|the reader should know some Python and NumPy|
ABXC is a range of nonsmooth optimization problems from Curtis et al.;
see the problem description and links in
They're hard to optimize, very noisy near minima, and some are infeasible.
Here's a plot of
creeping up an infeasible slope:
MOPTA08 is a test case for optimization from D.R. Jones, "Large-scale multi-disciplinary mass optimization in the auto industry" , 2008. It has 124 variables in 0 .. 1, a linear objective function, and 68 nonlinear constraints.
The purpose of
mopta08-py is to try out various optimizers -> python -> MOPTA08,