[graycat]

The point of the post is that in my long experience there was not much about optimization that in any significant practical career sense was "applied", i.e., no jobs even to keep one from living on the streets, far from a career to buy a house and support a family.

The point about my Ph.D. with a lot in optimization is that I was quite well qualified in the field, but even with all those qualifications "applied" was not real, i.e., there just was nothing like a career in optimization applications.

Can suspect that, yes, "Ph.D." did seriously damage all career prospects, optimization, math, even computing.

The successes I did have were in computing. At the time the math was a small aid.

Did get paid for some work in applied optimization on some military problems. Looking back, there was some stranger in the office eager to discuss the weather, etc. with me. Maybe I said the wrong things about some of the US foreign wars, and then the stranger was gone. Might have been some high end military job interview that needed only gung ho attitudes toward foreign policy.

Delicate political situation, and I was oblivious about politics. Like, "stick to two subjects, the weather and everyone's health" and avoid "sex, politics, and religion".

Early on while I was in a grad program teaching math, some recruiters came from DC desperate for anyone with some math/physics education. I interviewed: Got a offer and took a job right away. Soon bought a new car and got married.

In those days, around DC, to get a job, just look in the WaPo, apply, go on the interview, show some knowledge of some of computing, get an offer, compare a few offers, with, say, a 15% raise, and accept one -- worked great. For a while, at GE time sharing national HQ, was the main guy for the applied math library, e.g., the FFT, regression analysis. Later a good background in "applied" optimization, worthless.

[richrichie]

You should have tried wall street. At this point they are the real supporters of mathematicians. We had optimization problems everywhere and had physics PhDs reinventing mathematical algorithms and keeping things “proprietary”. Right now i work in a startup that essentially writes optimization routines for portfolio problems.

I will blame your phd advisor.

[graycat]

Wall Street?

I was in NY and close enough to NYC. I'd just published a paper in anomaly detection in complex systems, gave a talk at the main NASDAQ server farm, and later at Morgan Stanley. No real interest.

Sent a copy of my anomaly paper to a hedge fund, got an interview, was asked by one of their junior people "If know the correlation between A and B and that between B and C, what about A and C"? Okay, maybe: Start with the cosine of the sum of two angles???

Asked them for a reference on investing math -- did already know about the old Markowitz work, the efficient frontier, the role of quadratic optimization, about to do more with stochastic differential equations for the Black-Scholes work -- got the book they mentioned, saw that its math was all junk, and didn't follow up with the A and C contact, a mistake.

Did send a resume to Simons.

Looked into deterministic optimal control, Athens and Falb, talked with Athens, later talked with an Athens student who knew something about Simons and claimed that he hired mostly Russian mathematicians. So gave up. A mistake. I was naive.

Later, of course, Simons explained that he liked people who, say, via math but any math, had shown some ability, and I had some evidence I could have shown. My Math SAT was high enough that maybe I even beat Simons?

I was naive: Assumed that a carefully written resume was necessary and sufficient and that anything else was superfluous and unwelcome.

Nope: In practice in the real world, keep trying different things. Do send reprints of published papers. E.g., when I was at Georgetown, computer center staff and teaching computer science, a prof had some teaching software as a front end to the IBM SSP (scientific subroutine package) and in testing found that two of the IBM routines were too slow and the third had poor numerical accuracy. So, I wrote plug compatible versions -- used some (n)ln(n) software and some tricky double use of memory to replace the n^2 software and used some Forsythe and Moler work to fix the accuracy problem -- seemed too simple to me, but COULD have sent Simons that work. Once did get a lecture on differential geometry from a student of A. Gleason and had a copy of some S. Chern notes -- could have studied those and sent something to Simons. How'd I know Simons knew Chern??

There is a recent remark: "Don't give up. Keep plugging".

I was naive. Knew much more about math and computing than people and personality.

Since WWII, the US military has pushed hard to have more -- students, professors, and research -- in math and science. In high school, taught myself the math, learned the physics at a glance, otherwise goofed off (had a girlfriend drop dead gorgeous), but did well on the state standardized tests, so got sent to summer math/physics enrichment programs. I swallowed the bait hook, line, and sinker. I'd recommend:

"Always look for the hidden agenda."

"Believe none of what you hear, half of what you see, still that will be twice too much."

"Who you know can be more important than what you know."

There were some opportunities to "know" some powerful people, but I was naive.

My Ph.D. advisor was a nice guy, but I got to him after the fallout of a bad civil war in the faculty and never much talked with him. For my dissertation, some applied math, and had the main idea on an airplane flight before the Ph.D. program, in the first year wrote a 50 page first draft, later cleaned up the math, used Fubini's theorem in a short proof that my math was optimal, wrote some illustrative software, typed in the paper, showed it to my advisor and the rest of the department, had a famous guy a Chair of an orals committee to review the dissertation, and graduated. My advisor and one of the faculty (connected in DC and later President at an Ivy) knew a LOT about politics, but I was naive.

For a while, my career, in computing but with some math on the side, e.g., the FFT and digital filtering of Navy sonar signals, was going well, so I got the Ph.D. in applied math just to do better in THAT career and with ZERO intentions to be a professor or do academic research. That career direction was MY idea, mine alone, ..., a BAD situation!!! I was naive.

[whiterknight]

> I was naive. Knew much more about math and computing than people and personality.

Do you think not learning math would have helped you understand people at a younger age? It sounds like you just needed time to grow socially and in practicality. For most people on this forum, that’s a challenge regardless.

[graycat]

About people in math and the more technical parts of computing, I've guessed that poor socialization has played a role.

But when my career was okay, it was in computing, and I did well enough in the socialization.

Can consider these and those issues, but my experience was that "applied" optimization, as in the book title in the OP here, was too near the empty set.

It isn't just me: My professors in applied math and the ones in optimization were not getting much if anything in consulting. I've been recruited and hired, but never for optimization.

Here I'm trying to do a service to the readers: Be very careful about the idea that there is significant career help via "applied" optimization.

[whiterknight]

Sounds like you opened up the newspaper and scanned for “mathematician”. Leveraging phd research into a great job is a tough. Re-skilling into a normie engineer/technician/analyst, is not.

My point is not to criticize your job hunting skills, it’s to suggest that this an undue psychological burden in your life and is perhaps masking other causes and personal challenges.

[graycat]

Naw: The WaPo period was before my Ph.D. The ads were for computing -- math not mentioned. For some years, the career was computing but with some math, e.g., the FFT (fast Fourier transform), ....

I never wanted the Ph.D., what I learned there, the research I did there, to be the basis of a career. Instead, before the Ph.D. I had a good career going with computing and, at times a crucial help, some math, and went for the Ph.D. ONLY to do better at THAT career. For my career, the day I entered the Ph.D. program was a BIG step down, and what I'd learned about optimization was, in a word, WORTHLESS.

My main point here is on the word "applied" for optimization: I was well qualified, and happened to publish some research in optimization, but discovered that "applied" optimization was not the basis of a good career. Here I'm just reporting that fact. I doubt that there is still any real career opportunity in "applied" optimization.

So, a book title with "Applied" Optimization is to me a outrage.

I wasn't stuck on "optimization". For a while worked in the first wave of AI (artificial intelligence via the Rete algorithm). Then published in mathematical statistics. I was perfectly willing to mow grass, shine shoes, ..., do anything that would support me financially, be reasonably safe, and not seriously illegal but discovered that "Ph.D." on the resume blocked any such. Thought about taking "Ph.D." off the resume but was afraid that I'd get into trouble due to the gap in time.

Here my point, complaint, warning, contribution to others, is: My long experience was that there is nearly no career in "applied" optimization. A second point could be, outside of academics, a Ph.D. can hurt your career. Try leaving it off your resume. A Ph.D. might be worse for your career than a felony conviction; no joke (my legal history is totally clean).

In life, we are forced to make important decisions without good information. In my career, at times I did well, and at times I didn't.

E.g., by middle school it seemed accepted and true that education helps, more education helps more, education in the STEM fields is the best, a Ph.D. is the best education, and, thus, a Ph.D. in a STEM field should be really good, e.g., easily enough to buy a house and support a family.

Truth: Nope, too simple. I couldn't take care of my wife, kitty cats, get a job, any job, at all, ANY job, got run out of the house by the Sheriff with guns.

With a BS "With Honors" in math, I got strongly recruited. With a Ph.D. in applied math, including optimization, I got strongly rejected.

Yup, it hurt. I was manipulated, lied to, and hurt.

"psychological burden": Maybe those are the right words. But millions of people have suffered worse, e.g., The Great Depression, wars, Covid in the family, and much more, and still did well.

Don't know the solution in general.

For me, now, still good in math and computing, with .NET, etc. got a Web site, with some math at the core, running easily enough, and intending to go live, get some viewers, run simple ads (standard sized rectangles), and make some money. In this, want to remain anonymous and not be a public person.

And want to OWN the business. Have someone list what papers I need to file for a business, an LLC, etc. Get an accountant. Get and receive revenue. In simple terms, add up the expenses and keep the rest. Eventually sell the business and pursue, say, mathematical physics.

[whiterknight]

> I doubt that there is still any real career opportunity in "applied" optimization.

I agree there are no ready made jobs for that.

But you yourself know there are optimization problems all over real life. It’s a sales problem. Companies don’t know what they need or who has it.

> there is nearly no career in "applied" optimization

Agreed. But that’s true of all PhDs. The only difference is business guys see “computer science” and have an idea of where it fits in their org. It’s easier to sell. But in reality there is no business for experts in complexity theory or category theory type systems.

Making money involves solving practical problems. Even professors take a two job approach, mixing official research to get tenure with stuff they are actually interested in.

> With a BS "With Honors" in math, I got strongly recruited

This is very unfortunate. Because professors grew up competing in an academic tournament for their jobs they think that’s how the whole world works.

[graycat]

Correct!