[mlpack] mlpack Digest, Vol 49, Issue 56

Mon Mar 26 11:57:14 EDT 2018

Hello, Artyom Lyan

The conversations bettwen @Stephentu and @rcurtin in this issue may be
helpful. https://github.com/mlpack/mlpack/issues/370

After read the papers again, I do think the algorithm may wander around
stationary point, which may be related to some parameters or initialization.

Best regards,
Kaiqiang

On Mon, Mar 26, 2018 at 11:06 PM, <mlpack-request at lists.mlpack.org> wrote:

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> Today's Topics:
>
>    1. Re: Fix MVU+LRSDP in GSoC 2018 (kaiqiang Xu)
>    2. Regarding Proposal for Reinforcement Learning (Amit Panghal)
>    3. GSOC (aditya mitkari)
>    4. gsoc proposal (Артём Лян)
>    5. Paper Describing Saddle Points in LRSDP (Abhijeet Krishnan)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 26 Mar 2018 01:28:18 +0800
> From: kaiqiang Xu <rickllykqxu at gmail.com>
> To: mlpack_maillist <mlpack at lists.mlpack.org>
> Subject: Re: [mlpack] Fix MVU+LRSDP in GSoC 2018
> Message-ID:
>         <CABuaWqzMFc7h_h-44d99dkigEa+7fT01geR7Lp392sNKKwyhNQ at mail.
> gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hello,
>
> After I went through all the issues about MVU and LR-SDP, in particular,
> #370 <https://github.com/mlpack/mlpack/issues/370>, I find that LR-SDP is
> really a tough guy. It is necessary to summarise experiences of @rcurtin
> and @stephentu.
> I will dig into it, read source code and update my proposal.
>
> Best regards,
> Kaiqiang
>
> On Sat, Mar 24, 2018 at 7:05 AM, kaiqiang Xu <rickllykqxu at gmail.com>
> wrote:
>
> > Hi, Ryan
> >
> > Sorry to borther you directly by mistake in last email.
> > I modified proposal and emphasize approaches. Now it has been submited to
> > GSoC. Can you check it and give me some feedback  if available?
> >
> > Best regards,
> >
> > Kaiqiang
> >
> >
> > 2018-03-23 22:41 GMT+08:00 Ryan Curtin <ryan at ratml.org>:
> >
> >> On Fri, Mar 23, 2018 at 04:21:36PM +0800, kaiqiang Xu wrote:
> >> > I have read the papers recommended under ideas-page, and been
> >> fascinated by
> >> > their formulations, variants(e.g. MFNN) and implementions.
> >> >
> >> > Firstly, MVU/MFNU is a powerful method to reduce high dimensional data
> >> > which can be viewed as a more general PCA version. The paper mentions
> >> that
> >> > MVU/MFNU need to deal with all-nearst neighbor computation and
> >> > optimization. Especially, a technique based on dual-tree and L-BFGS to
> >> > solve the non-convex formulation of MVU allows MVU more scable.
> >> >
> >> > Secondly, SDPs is a definination to a class of optimization problems,
> >> and
> >> > interior point method applying to it is fast and converged. But
> >> scalability
> >> > is an issue. So taking advantage of low rank property of matrix, the
> >> > low-rank reformulation of SDPs can be solve via Burer-Monoteiro
> method.
> >> > Especially, in some conditions, the Burer-Monoteiro can get global
> >> optimal.
> >> >
> >> > Is there any error with respect to above summary? I'm glad to hear
> your
> >> > advice.
> >> >
> >> > Here I formulate some ideas:
> >> >
> >> >    1.
> >> >
> >> >    Guarantee the correctness of MVU, which optimized by convex
> >> optimization
> >> >    techniques.
> >> >    2.
> >> >
> >> >    Check the correctness of implementation of LRSDP. Design several
> >> special
> >> >    problem, such as the Lovasz theta SDP, the maximum cut SDP
> >> relaxation and
> >> >    etc. Solving them by LRSDP and the spectral bundle method of
> >> Helmberg as
> >> >    well as dual-scaling interior-point method of Benson mentioned in
> >> paper *A
> >> >    Nonlinear Programming Algorithm for Solving Semidefinite Programs
> via
> >> >    Low-rank Factorization* .
> >> >    3.
> >> >
> >> >    Check the convergence of LRSDP under conditions mentioned in *The
> >> >    non-convex Burer–Monteiro approach works on smooth semidefinite
> >> programs*
> >> >    . In paper it should go convergence. Some special case should be
> >> created to
> >> >    test this idea.
> >> >    4.
> >> >
> >> >    Concatenate MVU and LRSDP, small dataset should be prepared to
> test.
> >> >    Disable parameter auto-tunning, then check the convergence and
> >> compare its
> >> >    result with idea(1) or other tools. If there is any problem
> >> happended, dig
> >> >    into the optimization section. Hand computation is needed. My
> >> intrests in
> >> >    optimization makes me never afraid of computation.
> >> >    5.
> >> >
> >> >    if idea(4) is convinced, a big dataset should be employed to MVU
> and
> >> >    LRSDP. Some extreme case may happened such as traping into local
> >> minima.
> >> >    Here the paper applying MVU to time speach dataset may help me out.
> >> >
> >> > How do you think of the ideas above? Can you give me advice?
> Obviously I
> >> > have great passion to solve it. Later hours I will submit my draft of
> >> > proposal to GSoC, may you can give me some feedback.
> >> >
> >> > The paper *Local Minima and Convergence in Low-Rank Semidefinite
> >> > Programming* is very hard to understand. I will go through it with my
> >> > professor occupied optimization. I believe I can come up with new
> ideas
> >> and
> >> > improve my plan.
> >>
> >> Hi Kaiqiang,
> >>
> >> It sounds like you have formulated a good plan.  I would suggest making
> >> your approach clear in your proposal, especially with what you will do
> >> if MVU+LRSDP does not converge (since I do not expect it to converge).
> >>
> >> Thanks,
> >>
> >> Ryan
> >>
> >> --
> >> Ryan Curtin    | "Wha' happened?"
> >> ryan at ratml.org |   - Mike LaFontaine
> >>
> >
> >
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> ------------------------------
>
> Message: 2
> Date: Sun, 25 Mar 2018 16:38:38 -0400
> From: Amit Panghal <ap5422 at nyu.edu>
> To: mlpack at lists.mlpack.org
> Subject: [mlpack] Regarding Proposal for Reinforcement Learning
> Message-ID:
>         <CABGV1BGxF4EB+ikJM9MmmAQ5uQhaZjN8g56-
> BDbnMhz6fO0OFg at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hi Mentors,
>
> I am 1st year graduate student in computer science at New York University.
> I had spent significant amount of time going through RL papers  to draft a
> proposal for RL algorithm to play Atari games. I have a rough understanding
> of code structure in ml-pack. I have a draft proposal ready for
> implementing double DQN. I am bit confused regarding, do I need to
> implement the agent which using gym api's to interact with environment and
> uses the current framework(
> https://github.com/mlpack/mlpack/blob/master/src/mlpack/
> methods/reinforcement_learning/q_learning.hpp)
> Or implement double dqn , PPO algorithms , Persistent Advantage Learning
> DQN. Isnt double DQN and DQN, already implemented?
>
> regards,
> Amit Panghal,
> Courant Insitiute of Mathematical Sciences,
> New York University
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> Message: 3
> Date: 25 Mar 2018 20:40:37 -0000
> From: "aditya mitkari" <aditya_mitkari at rediffmail.com>
> To: <mlpack at lists.mlpack.org>
> Subject: [mlpack] GSOC
> Message-ID: <20180325204037.5409.qmail at f5mail-224-151.rediffmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Can the PCA API code be considered for parallelization using openMP?
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> ------------------------------
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> Message: 4
> Date: Mon, 26 Mar 2018 10:52:05 +0300
> From: Артём Лян <tntlagf93 at mail.ru>
> To: mlpack <mlpack at lists.mlpack.org>
> Subject: [mlpack] gsoc proposal
> Message-ID: <1522050725.875712426 at f191.i.mail.ru>
> Content-Type: text/plain; charset="utf-8"
>
> Hello mlpack mentors.
> My name is Lyan Artyom.
> Could you please review my proposal, that uploaded as draft at
> summerofcode.withgoogle.com
> Thanks in advance.
>
>
> --
> All the best,
> Artyom Lyan
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> ------------------------------
>
> Message: 5
> Date: Mon, 26 Mar 2018 11:06:19 -0400
> From: Abhijeet Krishnan <abhijeet.krishnan at gmail.com>
> To: "mlpack at lists.mlpack.org" <mlpack at lists.mlpack.org>
> Subject: [mlpack] Paper Describing Saddle Points in LRSDP
> Message-ID: <5ab90c6d.04ed0d0a.133c9.ba3a at mx.google.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hi Ryan,
>
> You had mentioned a tech note/paper by Sam Burer describing saddle points
> in LRSDP. I sent him an email regarding the same, and he believed this
> paper was being referred to. Is it the right one?
>
> Regards,
> Abhijeet Krishnan
>
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