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Intro to Multicriteria Optimization

69 点作者 Zephyr314将近 9 年前

6 条评论

mccourt将近 9 年前
I wrote this post and am also happy to comment. Hopefully we'll be following this up soon with a post on treating robustness and cost simultaneously in a multicriteria setting. Also, special thanks again to Devon Sigler at the University of Colorado Denver for his help editing this post.
oli5679将近 9 年前
For anyone looking for a free optimization tool in python, sypy.opimize is easy to use.<p>Eg if I have a complicated function revenue([A,B,C,D])<p>I can define obj([A,B,C,D]) = -1* revenue([A,B,C,D])<p>and use:<p><pre><code> &gt;&gt;&gt;import numpy as np &gt;&gt;&gt;from scipy.optimize import minimize &gt;&gt;&gt;X0 = np.array([1.5, 0.7, 1.2, 100]) &gt;&gt;&gt;options={&#x27;xtol&#x27;: 1e-4, &#x27;disp&#x27;: True}) &gt;&gt;&gt;X* = minimize(obj, x0, method=&#x27;nelder-mead&#x27;, options) </code></pre> <a href="http:&#x2F;&#x2F;docs.scipy.org&#x2F;doc&#x2F;scipy&#x2F;reference&#x2F;tutorial&#x2F;optimize.html" rel="nofollow">http:&#x2F;&#x2F;docs.scipy.org&#x2F;doc&#x2F;scipy&#x2F;reference&#x2F;tutorial&#x2F;optimize....</a>
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apathy将近 9 年前
I guess the novelty here is that constraint based optimization with no guarantees on convexity or biconvexity is a PITA?
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Zephyr314将近 9 年前
I&#x27;m one of the co-founders of SigOpt (YC W15) and am happy to answer any questions about SigOpt or the methods we apply.<p>More info on our research (and examples) can be found at <a href="https:&#x2F;&#x2F;sigopt.com&#x2F;research" rel="nofollow">https:&#x2F;&#x2F;sigopt.com&#x2F;research</a>
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bumbledraven将近 9 年前
&gt; The reason this is a more complicated situation is that an ordering of vectors in RkRk does not exist... how would one order the vectors u = (1,2,3), v = (2,1,3), w = (3,2,1)?<p>It seems straightforward to order those vectors by first comparing the first component, then the second, then the third. The result is u,v,w. It&#x27;s as if you wanted to sort a multi-column report in Excel. What am I missing?
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orasis将近 9 年前
Here is a much simpler approach that works for many problems: minimize the distance to a desired multi-variate state.<p><a href="https:&#x2F;&#x2F;medium.com&#x2F;@justchap&#x2F;using-the-pythagorean-theorem-to-model-complicated-goals-in-machine-learning-b85f04b34ad4#.9tajh1t0l" rel="nofollow">https:&#x2F;&#x2F;medium.com&#x2F;@justchap&#x2F;using-the-pythagorean-theorem-t...</a>
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