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11h
comment proof that length of difference of projections implies equality of length of normals
@Blind: Thank you but I need to understand it first and then I will definitely accept it. But I don't follow all the steps yet. I'll get back to you if I can't figure them ( the ones I don't follow yet ) out myself.
11h
accepted proof that length of difference of projections implies equality of length of normals
20h
comment proof that length of difference of projections implies equality of length of normals
Thank you. I will print out and go over carefully and then check the answer. It's appreciated.
21h
comment proof that length of difference of projections implies equality of length of normals
@Batman: Thank you for the hint. I will try that approach. It's appreciated.
21h
comment proof that length of difference of projections implies equality of length of normals
Also, if anyone knows of a book or notes where topics like the one in this proof are explained in detail for "dummies", ( i.e: not rockefellar or lemarchal ) and where there might be figures to aid in the understanding, it's appreciated.
21h
comment proof that length of difference of projections implies equality of length of normals
@Robert: Thanks. I should have said projection onto a closed convex set. Apologies for that also.
21h
comment proof that length of difference of projections implies equality of length of normals
@David.K: thanks. I had a typo in the final line. My apologies.
21h
revised proof that length of difference of projections implies equality of length of normals
deleted 1 character in body
1d
asked proof that length of difference of projections implies equality of length of normals
Jan
31
asked showing something supposedly obvious in the proof of the fletcher-powell algorithm
Jan
21
comment understanding a statement in Gill, Murray and Wright “Practical Optimization”
Very nice. Thank you daw. I checked the answer.
Jan
21
accepted understanding a statement in Gill, Murray and Wright “Practical Optimization”
Jan
21
revised understanding a statement in Gill, Murray and Wright “Practical Optimization”
added 31 characters in body
Jan
21
asked understanding a statement in Gill, Murray and Wright “Practical Optimization”
Jan
17
asked why not handle box-constraints with a transformation
Nov
23
comment proving that the ewma is convex
thanks michael. I get it now. I looked quickly at the paper without reading it carefully. and your explanation was really great. unfortunately, I can't assume that my $X_t$ are zero or positive which means it's not convex which means that there can be local minimums which is not good when doing optimization over $\lambda$.
Nov
23
comment proving that the ewma is convex
michael: I think I understand now. The sum of convex functions is convex but you're not saying that $(1-\lambda)\lambda X_t$ is a convex function of k for ALL $X_t$ but rather Just for $X_t = 1$. Is that the correct understanding ? Thanks again and I do appreciate your help.
Nov
23
comment proving that the ewma is convex
hi michael: I'm sorry for the dumb question but, if the sum of convex functions is convex, then isn't the sum convex which is the function I referred to in my initial email ? I guess it doesn't but if you could explain that ( my brain and math don't always see eye to eye :) ) , it's appreciated. Mark
Nov
22
comment proving that the ewma is convex
Hi Michael: Thank you for your input. The link of the paper I refererred to is below. The statement is on page 7 and the paragraph starts with the word : "Nevertheless". I don't doubt that you are correct but I am going to simulate the process and use the definition of convexity with different values of t_1 and t_2 to see it in action. Thanks again and I'll let you know what comes from that exercise.er.ethz.ch/publications/MAS_Johan_Boissard_Dec12.pdf
Nov
22
comment proving that the ewma is convex
Hi Michael: I read the statement in someone's thesis but it could be incorrect. My apologies. Also, some define it without the $1-\lambda$ term so if that's causing the lack of convexity, that would be why.