Archive for March 2009
A new version of GLPK (version 4.37) has been released. The new version includes (among other features) a 0-1 feasibility pump, a heuristic to find feasible solutions to 0/1 programs which is also available in the standalone solver glpsol. Moreover, the graph API has been extended (see below).
GLPK 4.37 — Release Information
Release date: Mar 29, 2009
GLPK (GNU Linear Programming Kit) is intended for solving large-scale
linear programming (LP), mixed integer linear programming (MIP), and
other related problems. It is a set of routines written in ANSI C and
organized as a callable library.
In this release:
The 0-1 Feasibility Pump heuristic was included in the GLPK
integer optimizer glp_intopt. On API level the heuristic can be
enabled by setting the parameter fp_heur in glp_iocp to GLP_ON.
This feature is also available in the solver glpsol through
command-line option ‘–fpump’. For more details please see the
reference manual included in the distribution.
The following new API routines were added:
glp_print_sol write basic solution in printable format
glp_print_ipt write interior-point solution in printable
glp_print_mip write MIP solution in printable format
glp_read_graph read (di)graph from plain text file
glp_write_graph write (di)graph to plain text file
glp_weak_comp find all weakly connected components
glp_strong_comp find all strongly connected components
The following API routines are deprecated: lpx_print_sol,
lpx_print_ips, lpx_print_mip, lpx_print_prob (the latter is
equivalent to glp_write_lp).
A bug was fixed in the interior-point solver (glp_interior) to
correctly compute dual solution components when the problem is
The files configure.ac and Makefile.am were changed:
(a) to allow using autoreconf/autoheader;
(b) to allow building the package in a directory other than its
Thanks to Marco Atzeri <firstname.lastname@example.org> for bug report.
An example model in the GNU MathProg language was added.
Thanks to Larry D’Agostino <Larry.D’Agostino@gmacrescap.com> for
See GLPK web page at <http://www.gnu.org/software/glpk/glpk.html>.
A new GUSEK version (that includes the new GLPK release) is available as well:
I’ve updated the Gusek project on SourceForge:
Release 0.2.4 changes:
Bug correction: now “Stop Execution” really finish glpsol.exe process.
GLPK updated to 4.37.
Gusek provide an open source LP/MILP IDE for Win32,
packing a custom version of the SciTE editor linked to the GLPK
standalone solver (glpsol.exe).
I just saw this really impressive TED talk.
This demo — from Pattie Maes’ lab at MIT, spearheaded by Pranav Mistry — was the buzz of TED. It’s a wearable device with a projector that paves the way for profound interaction with our environment. Imagine “Minority Report” and then some.
About Pattie Maes
At the MIT Media Lab’s new Fluid Interfaces Group, Pattie Maes researches the tools we use to work with information and connect with one another. Full bio and more links
About Pranav Mistry
Pranav Mistry is the genius behind Sixth Sense, a wearable device that enables new interactions between the real world and the world of data. Full bio and more links
A few days ago, Stephen Wolfram announced his Alpha project. The system is supposed to be something like the next level of search engines. A few commentators that were actually able to get a demonstration say it might be even bigger than google – ok, we have heard this several times before but anyways…
Rather than looking up information it calculates the answer to a question by using a vast amount of data and ‘hand-engineered scripts’:
In a nutshell, Wolfram and his team have built what he calls a “computational knowledge engine” for the Web. OK, so what does that really mean? Basically it means that you can ask it factual questions and it computes answers for you.
It doesn’t simply return documents that (might) contain the answers, like Google does, and it isn’t just a giant database of knowledge, like the Wikipedia. It doesn’t simply parse natural language and then use that to retrieve documents, like Powerset, for example.
Instead, Wolfram Alpha actually computes the answers to a wide range of questions — like questions that have factual answers such as “What is the location of Timbuktu?” or “How many protons are in a hydrogen atom?,” “What was the average rainfall in Boston last year?,” “What is the 307th digit of Pi?,” or “what would 80/20 vision look like?”
Think about that for a minute. It computes the answers. Wolfram Alpha doesn’t simply contain huge amounts of manually entered pairs of questions and answers, nor does it search for answers in a database of facts. Instead, it understands and then computes answers to certain kinds of questions.
A extensive review can be found here and there is also a comment from Doug Lenat, the (main) developer of Cyc, the “artificial intelligence project that attempts to assemble a comprehensive ontology and knowledge base of everyday common sense knowledge, with the goal of enabling AI applications to perform human-like reasoning.”
Having just discussed this with a few of my colleagues doing finance and risk management, one of the first questions that arose was if this knowledge computation can be turned into a trading strategy. Something like a knowledge hedge fund. I guess this would be indeed an interesting question, i.e., if these algorithms can be turned into something like a predictor for all kinds of things. Imagine a question “How much will the houseprice in my neighborhood XYZ suffer from the downturn?”, or “What is the average price for a ticket from say New York to Hong Kong?”.
A few articles/blog posts that might be in particular interesting:
- In reference to the Wired article Recipe for Disaster: The Formula That Killed Wall Street:
Finance’s Gaussian Copulas: The New Frankenstein Monster (Aurelie Thiele)
Finance’s Gaussian Copulas, Part 2 (Aurelie Thiele)
- In Plato’s cave — Mathematical models are a powerful way of predicting financial markets. But they are fallible (The economist)
After Madoff’s client list had been published (see also full Madoff coverage at WSJ) several maps visualizing the investor network (here) as well as their geographic locations (WSJ, madoffmap, geocommons) emerged. These maps might give some feeling for the feeding mechanism as well as the extend of this “operation”.
(see also Mike Trick’s original post)
While browsing through nature I stumbled upon an article titled ‘Towards responsible use of cognitive-enhancing drugs by the healthy‘ (a pdf is also available here). The authors basically advocate a more open approach to brain enhancing drugs:
Society must respond to the growing demand for cognitive enhancement. That response must start by rejecting the idea that ‘enhancement’ is a dirty word, argue Henry Greely and colleagues.
Today, on university campuses around the world, students are striking deals to buy and sell prescription drugs such as Adderall and Ritalin — not to get high, but to get higher grades, to provide an edge over their fellow students or to increase in some measurable way their capacity for learning. These transactions are crimes in the United States, punishable by prison. Many people see such penalties as appropriate, and consider the use of such drugs to be cheating, unnatural or dangerous. Yet one survey estimated that almost 7% of students in US universities have used prescription stimulants in this way, and that on some campuses, up to 25% of students had used them in the past year. These students are early adopters of a trend that is likely to grow, and indications suggest that they’re not alone.
In this article, we propose actions that will help society accept the benefits of enhancement, given appropriate research and evolved regulation. Prescription drugs are regulated as such not for their enhancing properties but primarily for considerations of safety and potential abuse. Still, cognitive enhancement has much to offer individuals and society, and a proper societal response will involve making enhancements available while managing their risks.
Frankly, I am not sure what I should think about this – actually, I guess I do not even want to think about it and its implications. I mean operations research and optimization are, well, about optimizing things. We optimize production schedules, frequency allocations, diets, and even sports. But optimizing or enhancing our brains? By taking drugs? Henry Greely and colleagues argue that society “must start by rejecting the idea that ‘enhancement’ is a dirty word”…. well, I guess I am not there yet.
So, I decided to spend some time today researching this development (rather than working on a conference abstract ;-( – I feel the guilt). I was even more surprised to learn that according to a survey conducted by nature (see also wired and others) about 20% of their readers (mostly researchers and scientists) (at least) tried to enhance their brain performance by using Ritalin, Adderall and Focalin – so called smart drugs. As most of these people are probably more on the smart side and educated enough to understand what they are doing, it would be too easy to dismiss their actions as a ‘glitch’ or a habit of some ill-guided drug addicts.
Apart from these invasive ‘treatments’ the market of smart games (i.e., games designed to improve the cognitive performance) is growing fast. From Nintendo’s Brain Age, PositScience to modified version of the n-back task (which is claimed to improve the fluid intelligence). Mental gyms are springing up (e.g. vibrant brains in San Francicso). According to Smart Brains, a research firm, “revenue from “brain-fitness software” reached $225 million in 2007″.
Similar for books on cognitive enhancement. For example, the Brain Rules book (and the website with video clips) from John J. Medina illustrates how our brain works and provides basic guiding principles of how to improve our brain performance by choosing the right setting, diet, rhythm etc. (Several articles on enhancing the cognitive performance can be also found on wired – see the “see also” section at the bottom). Techniques like mediation etc have been out there for hundreds of years.
Given all that, it seems obvious that there is a huge demand for brain enhancements which in turn might be understood as an indicator for the ever-rising pressure to excel. Given that trading smart drugs is a crime in the United States, punishable by prison, the (expected) effect of these drugs has to be rather significant (would you risk to go to jail for a cup of coffee?) and the pressure sufficiently high. The development is not completely new though: Jean-Paul Sartre and Paul Erdös took amphetamines to keep up with the incredible pace with which they were moving forward. On the other hand, the ethical impact of using smart drugs as well as the social effects are not well understood at this point. Will our children be forced to use smart drugs in order to keep up with the rest of their cohort? Do I have to take smart drugs to succeed?
The biggest problem that I see here, especially for the invasive treatments, is that we experiment with the most complex organ of the human body without having a thorough understanding of its mechanisms and thus of the effects that we cause.