Performance Optimization of Numerically Intensive Codes

I recommend a light book ( a slightly more than 150 pages) but with important content, which can be easily read in several days.



      

NSF Award: (Mixed) Integer and Combinatorial Optimization: New Convexification Techniques

A new NSF Award was recently given to Egon Balas on (Mixed) Integer and Combinatorial Optimization: New Convexification Techniques.

The objective of this research project is to investigate new ideas that could potentially further accelerate the revolution in integer programming. This project will investigate more efficient convexification techniques: a new cutting plane paradigm that generates a pool of points from which cuts can be produced, and a theory of cut-generating functions, both aimed at finding deeper cuts more efficiently. Integer programming has experienced a revolution in the last two decades that has greatly advanced our ability to solve practical problems in engineering, manufacturing, transportation, telecommunication, finance, marketing and many other areas of economic activity. According to recently performed extensive testing, integer programming solvers are now close to a billion times faster than they were twenty years ago. Better integer programming algorithms (including their linear programming components) account for a speedup of about half a million times, the rest of the improvement (by a factor of about 1600) coming from faster computers. A key element of this transformation was a breakthrough in the use of cutting planes in the early nineties, including the design of the lift-and-project cuts and the ensuing revival of the Gomory mixed integer cuts, two projects carried out by the principal investigators with previous NSF support.


Progress in the ability to solve large-scale mixed integer linear programs will affect problem solving and improve efficiency of operations in an extremely broad range of activities that include industrial production, supply chain management, logistics, transportation, electricity production, airport operations, telecommunication networks, health care applications such as scheduling intensive care units and determining radiation dosage, combinatorial auctions, finance and economics. The widespread impact of tools developed in this project will contribute to technological excellence and strengthen US technological leadership.

NSF Award: Efficiently Computable Convex Approximation Methods for Non-Convex Optimization Problems

A recent NSF Award is given to Luis Zuluaga on Computable Convex Approximation Methods for Non-Convex Optimization Problems.

This research project takes on the challenge of closing the large gap between the current ability to solve convex or integer-linear optimization problems and the capacity to solve non-convex optimization problems by introducing novel methodologies to solve this class of problems using polynomial optimization techniques. In particular, the research will be focused on the following three main directions. The first direction is the introduction of new polynomial optimization techniques that instead of solving non-convex problems exhaustively, do so dynamically. The second one is the introduction and effective use of polynomial optimization results that beyond semidefinite programming tools make full use of the powerful convex optimization toolkit. The third one is the use of polynomial optimization results, related to the convex reformulation of low-dimensional or low-degree non-convex problems, as the foundation to obtain better reformulations for general non-convex problems. This project's practically oriented research is aimed at solving key and difficult decision-making problems that are now regularly faced in the communications, healthcare, electricity, and oil and gas industries. Many times, the difficulty to solve these problems arises from the use of polynomials to closely model the behavior of real-world systems. Modeling real-world systems more accurately is an overarching need in today's competitive and resource-constrained environment.
The results of this research will substantially improve computer-based solvers for this class of problems. The software developed to achieve this goal will use open source software tools that will be made available to the public. The corresponding positive impact on profits, safety, resource management, client satisfaction, etc., will further broaden the practical successes that make Operations Research the "Science of the Better". Furthermore, the challenging nature of this research will provide ample opportunity to the participating graduate students to excel, and its results will be incorporated into appropriate graduate level courses.

CAREER: Stochastic Multiple Time-Scale Co-Optimized Resource Planning of Future Power Systems with Renewable Generation, Demand Response, and Energy Storage

A recent NSF Career award was given to Dr.Wu in Clarkson University in the area of smart grid, combining optimization and power system planning and scheduling.


This PI will develop co-optimized generation, transmission, and DR planning solutions to cope with the impacts of short-term variability and uncertainty of renewable generation (RG) and demand response (DR) as well as hourly chronological operation details of energy storage (ES) and generators. The approach is to (1) propose a stochastic multiple time-scale co-optimized planning model that explicitly integrates short-term variability and uncertainty as well as hourly chronological operation into long-term planning; (2) develop efficient solution methodologies and implement on high performance parallel computing facilities. Intellectual Merit: The interaction among variability, uncertainty, and constraints from long-term planning and hourly chronological operation will be quantified for enhancing security and sustainability of power systems with significant RG, DR, and ES. This research can be used to evaluate effective load carrying capability (ELCC) of variable energy sources, and to study policies on portfolios of energy production and storage techniques. This study is of practical importance since RG, DR, and ES are being implemented worldwide and their distinctive contributions to energy security and sustainability need to be well understood.

Broader Impacts: This project has profound impacts on the sound deployment of RG, DR, ES, and smart grid. For example, it allows better treatment of investment options which require transmission and generation together, in order to exploit favorable sites for wind or solar. The research and educational findings would help educate engineers to meet challenges of the secure and sustainable electricity infrastructure. The project will increase public awareness and understanding of the complexity of power system planning, and appeal to researchers and educators with interests in power systems-based research and education.

Post-Doctoral Position at IBM Research: (in Mathematical Programming)

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Post-Doctoral  Position at IBM Research: (in Mathematical Programming) ===================================================================
The Mathematical Programming  group at IBM Research invites applications for a post-doctoral  position. The initial contract is for one year but it can be renewed for a second year subject to mutual agreement. The expected starting date is the second half of 2013. 
The position is primarily a research position and the ideal candidate is expected to have a strong publication  record in mathematical optimization. Depending on the department activities at the time, there might also be opportunities to get exposed to applied optimization work jointly with other department members.
Our group offers a unique environment that combines basic research with applied optimization projects for our internal and external customers. Current members of the group include: Amirali Ahmadi, Francisco  Barahona, Sanjeeb Dash, Joao Goncalves, Oktay Gunluk, Aida Khajavirad and Leo Liberti. 
IBM T.J. Watson Research Center (http://www.watson.ibm.com/general_info_ykt.shtml) is located
30 miles north of New York City in Yorktown Heights, NY. 30 miles north of New York City in Yorktown Heights, NY. 
Interested candidates are invited to send their CVs with names of references to gunluk@us.ibm.comby February 28, 2013.
IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, orveteran status.
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Oktay Gunluk, Math. Sciences , IBM Research ----  gunluk@us.ibm.com, http://www.research.ibm.com/people/o/oktay/   

CAREER: Reduced-order Methods for Big-Data Challenges in Nonlinear and Stochastic Optimization

A recent NSF Career Award was given to Dr.Lan in University of Florida. Details of the award are below

The objective of this Faculty Early Career Development (CAREER) Program project is to develop a set of new reduced-order algorithms to tackle the big-data challenges in optimization. The last several years have seen an unprecedented growth in the amount of available data. While nonlinear, especially convex programming (CP) models are important to extract useful knowledge from raw data, high problem dimensionality, large data volumes and inherent uncertainty present significant challenges to the design of optimization algorithms. This research aims to attack these challenges by investigating: (i) novel first-order methods for deterministic CP that converge faster, require little structural information and do not rely on line search, based on level methods; (ii) stochastic first-order methods that handle data uncertainty in an optimal manner, based on stochastic approximation; (iii) novel randomization schemes for solving certain challenging deterministic CP problems beyond the capability of first-order methods; and (iv) stochastic first- and zeroth-order methods for general, not necessarily convex, stochastic programs. The research focuses on two fundamental issues across these topics: (i) the study of complexity which provides guarantees on algorithmic performance; and (ii) the exploitation of structures that leads to the design of algorithms with stronger complexity and superior practical performance.

If successful, a set of new algorithmic schemes will advance the state-of-the-art in nonlinear and stochastic optimization, bringing many practically relevant data analysis problems within the range of tractability. Example applications include algorithms for faster and more accurate medical image reconstruction and classification, which will be beneficial to healthcare. In addition, in seismology, effective stochastic programming methods will help to build predictive models by measuring thousands of earthquakes detected at seismic stations. The project will also support the PI's educational goals to improve students' learning in operations research, broaden the representation of underrepresented groups in the PhD program, and contribute to open research infrastructure through the development of optimization solvers.

Open position - Optimization expert with Jeppesen, Sweden

Optimization Expert to Jeppesen Gothenburg

Jeppesen’s crew and fleet solutions are mainly developed in Gothenburg, but with significant support from our offices in Montreal, Singapore, and New York. Jeppesen has more than 3,500 employees worldwide and is headquartered in Denver, Colorado, USA. Founded on the production of aeronautical charts, the company celebrated 75 years of guiding pilots safely to their destinations in 2009.

Jeppesen is a subsidiary of Boeing, the world’s leading aerospace company. Boeing is headquartered in Chicago, U.S.A., employing more than 160 000 people in 70 countries.
The successful candidate is goal-oriented and enjoys problem-solving, but still has an eye on the big picture. He or she must work well in an agile team and be very communicative and open. An interest in optimization and algorithms is a given. A strong software development background, especially in Object-Oriented programming is required. The new team member  will be working with highly talented and experienced developers, having regular interaction with the other development teams, installation projects and clients.
The successful candidate is goal-oriented and enjoys problem-solving, but still has an eye on the big picture. He or she must work well in an agile team and be very communicative and open. An interest in optimization and algorithms is a given. A strong software development background, especially in Object-Oriented programming is required. The new team member  will be working with highly talented and experienced developers, having regular interaction with the other development teams, installation projects and clients.
• PhD in Optimization/OR (or MSc + minimum 3 years of experience from developing similar applications) 
• Strong C/C++ skills
Meriting Experience
• Airline or Railway applications
• Simulation packages
• Statistical analysis
• Data mining 
• Python
Jeppesen Technology Services
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Jeppesen is looking to further strengthen the research and development team in airline crew and fleet optimization. We are solving some of the hardest combinatorial optimization problems you can imagine – in one of the most exciting and dynamic industries you can find. Delivering maximum value to our clients requires creativity, innovation and hard work. We believe there is no alternative to being number One – and that everyone can make a difference!

At Jeppesen in Gothenburg, over 300 employees from over 30 countries develop, implement and support cutting-edge resource optimization systems for airlines and railways. Clients include world-leading companies like Lufthansa, SAS, British Airways, Singapore Airlines, Delta Airlines, Virgin Atlantic Airways, Deutsche Bahn, Green Cargo and Qantas. Jeppesen has pioneered several new methods and algorithms for solving large, complex optimization problems, and has received significant recognition both in industry and academia, the latest by being awarded the INFORMS Prize in 2010.

As an Optimization Expert you will work with mathematical modeling, algorithmic development and performance tuning as well as taking part in research, design, implementation, testing and maintenance.

Requirements

Last day of application will be December 31, 2012.

Applications are only accepted through the Company website:

http://careers.jeppesen.com/JobDescription.asp?JobNumber=692783&lang=en

Tomas Gustafsson

Optimization & Research Strategist Crew & Fleet