Software development and Customer support position @ AMPL

AMPL Optimization is looking for an optimization expert to join
http://www.ampl.com/OPENINGS/2011July.html
Specific responsibilities will include the following:

• Create and solver interfaces, database handlers, and other AMPL-related software
• Supervise systems for software distribution, licensing, and invoicing, with activities including design, implementation, maintenance, and customer support
• Manage and develop AMPL's online availability through repositories & servers such as netlib, COIN-OR, NEOS, and future "cloud" offerings
• Assist in support of AMPL users and specific customer projects
• Participate in internal discussions on AMPL's development and marketing strategy

Qualified applicants will have the following preparation:

• Study of optimization and related topics in an MS, PhD, or comparable advanced degree program
• Experience carrying out research in optimization or pursuing applications of optimization to real problems
• Proficiency in software design and development using a variety of languages and platforms, with a willingness and ability to pick up new systems quickly
• Ability to communicate effectively (in person, over the phone, and in writing) with a variety of optimization users and customer representatives
• Interest in helping to run a business and in working independently

CHiPPS: can not find symbol MPI_Irecv with MPI solution

When configure the parallel version of CHiPPS project, people can encounter

 the error as below

Cannot find symbol MPI_Irecv with MPI 

which means that configure script couldn't link a simple executable that 

had a simgleMPI_Irecv() call in its main function. 

You can use mpicc -link_info to find those related linked library and 

add them in the option of configure. 

For example , the output of mpicc -link_info on one server shows that

gcc -g -O2 -g -Wall -O2 -I/usr/include/mpich2 -L/usr/lib -L/usr/lib

-lmpich -lopa -lpthread -lrt 

The corresponding configure options are 

../configure --enable-static --disable-shared --with-mpi-incdir=

/usr/include/mpich2 --with-mpi=lib

="-L/usr/lib -lmpich -lopa -lpthread -lrt" MPICC =mpicc MIPCXX =mpic++(mpiCC need 

to check the /usr/bin/executable)

CXXDEFS='-DMPICH_IGNORE_CXX_SEEK'

Visiting Assistant Professor Job at Stoney Brook University

Required Qualifications: PhD. in Operations Research Modeling or related discipline. Research areas to include mathematical programming, nonlinear optimization, stochastic optimization, combinatorial optimization, and applications of optimization.

Preferred Qualifications: A record of achievement to include published papers, an established reputation for research excellence, visibility within the research community, and experience writing proposals and working on funded research projects. The ideal candidate will have a strong potential for developing an externally funded research program.

Responsibilities & Requirements: The faculty member will participate in teaching and research. Research areas of interest include all areas of mathematical programming, optimization, modeling and simulation. The successful applicant will be expected to participate in collaborative modeling and simulation. The successful applicant will be expected to participate in collaborative research with faculty involved with Stony Brook Advance Energy Research and Technology Center (AERTC) in areas of renewable energy research, power production and distribution, and system optimization for Smart Grid Technology. Additionally, research collaborations may include areas of transportation optimization, sensor networks, wireless information technology, and quantitative finance.

Special Notes: This is a non-tenure track position. FLSA Exempt position, not eligible for the overtime provisions of the FLSA. Internal and external search to occur simultaneously. **Applications will be accepted until the position is filled.

The selected candidate must successfully clear a background investigation.

Announcing BuildTools 0.7.0 release

he first release of the BuildTools 0.7 series has just been created.
Changes w.r.t. 0.6 can rougly be seen here:
https://projects.coin-or.org/BuildTools/changeset?reponame=&new=2365%40trunk&old=1950%40trunk

Incomplete release notes are below. Some of the fixes may also have been
merged to the 0.6 series.

In case of questions, refer also to the updated documentation page for
project managers at https://projects.coin-or.org/BuildTools/wiki/pm-main
If your project is still using BuildTools 0.5 and you consider to
upgrade to use 0.7, have a look into the brief tutorial on switching
from 0.5 to 0.7:
https://projects.coin-or.org/BuildTools/wiki/pm-switch


coin.m4:
- changed default installation directory for libraries back to ${prefix}/lib
- removed macros that were marked deprecated in BuildTools 0.6
- removed ADDLIBS, FADDLIBS, and PCADDLIBS variables; macros like
COIN_CHECK_GNU_ZLIB now take a list of prefixes for shell variables
where to add corresponding linker flags
- defines for numeric version numbers of the form PRJCT_VERSION_MAJOR,
PRJCT_VERSION_MINOR, PRJCT_VERSION_RELEASE are now created automatically
by configure
- added recognition of Windows 64bit MKL libraries and MacOS X Veclib
framework as Blas/Lapack alternatives
- --enable-maintainer-mode should work again
- new macros COIN_CHECK_ISFINITE and COIN_CHECK_ISNAN (originally from
CoinUtils) to check for variations of isfinite and isnan functions and
to define suitable C preprocessor macros COIN_C_ISFINITE and COIN_C_ISNAN
- configure help messages about enable/disable commands have been
changed to say --enable-xyz for features that are disabled by default
and vice versa
- --without-xyz, --without-xyz-lib, --without-xyz-incdir,
--without-xyz-datadir and corresponding --with-xyz*=no should work to
disable the use of a particular package
- added -O0 to compiler flags in debug mode
- added -Wno-long-long to compiler flags
- removed -Wimplicit from C++ compiler flags
- slightly change the way the FLIBS variable is setup (fixes some issue
on MacOS X)
- change the order in which to check for Fortran compiler, e.g.,
Fortran90 compilers are prefered to Fortran77 compilers
- enable check for Fortran cross-compiler
- only pkg-config versions >= 0.16 are supported now
- $prefix/share/pkgconfig has been added to the search path for .pc files
- improved setup of XYZ_DEPENDENCIES variable for use in Makefiles to
specify cross-project dependencies of a library; added option
--disable-interpackage-

dependencies to disable setup of XYZ_DEPENDENCIES
variable
- allow to exclude package versions via != in AC_COIN_CHECK_PACKAGE
- fix parsing of .pc files in mingw builds
- fix setup of XYZ_LIBS_INSTALLED variables for builds with cl compiler

{prepare,commit}_new_{release, stable} scripts:
- improvements and fixes for computing the libtool age of a project
- adapted update of PRJCT_VERSION* defines in config header files to
changed scheme of these header files and to also update new defines
- the new release will not have a changed Dependencies file, but will
use the same as in the stable branch it originated from. Only the
svn:externals property is set to point to releases of Dependencies. This
makes it easier for a user to switch to updated releases via the
set_externals script.

headers/configall_system_msc. h:
- moved preprocessor defines that were specific for CoinUtils to the
CoinUtils configuration headers

Coopr 3.0 Release

We are pleased to announce the release of Coopr 3.0 (3.0.4362). Coopr is a collection of Python software packages that supports a diverse set of optimization capabilities for formulating and analyzing optimization models.

The following are highlights of this release:

- Solvers
  * More sophisticated logic for solver factory to find ASL and OS solvers
  * Various solver interface improvements
  * New Solver results object for efficient representation of variable values
  * New support for asynchronous progressive hedging

- Modeling
  * Changes in rule semantics to limit rule return values
  * Changes in the expected order of rule arguments
  * Constant sums or products can now be used as constraint bounds
  * Added full support for the ConstraintList modeling component.

- Usability enhancements
  * More explicit output from runph and runef commands
  * Added support in runef to write the extensive form in NL format
  * Add controls for garbage collection in PH

- Other
  * Efficiency improvements in generation of NL and LP files.
  * Significant efficiency improvements in parsing of Pyomo Data Files.
  * More robust MS Windows installer (does not use virtual python
  * environment)

Note that this is a major release of Coopr that changes the expected formulation of Coopr models.  See the Coopr blog for further details about deprecated functionality, which will be disabled in future releases.

See https://software.sandia.gov/trac/coopr/wiki/GettingStarted for instructions for getting started with Coopr.  Installers are available for MS Windows and Unix operating systems to simplify the installation of Coopr packages along with the third-party Python packages that they depend on.  These installers can also automatically install extension packages from Coin Bazaar.

Enjoy!

 - Coopr Developer Team
 - coopr-developers@googlecode.com
 - https://software.sandia.gov/trac/coopr/wiki/Documentation/Developers

NSF AWARD: Models and Algorithms for Risk Adjusted Optimization with Robust Utilities

Recently, a NSF funding is awarded to Sanjay Mehrotra. The introduction of the project is shown as below

The research objective of this award is to study concepts of relaxed univariate and multivariate stochastic dominance motivated from the specification of utility functions. A major challenge in risk-adjusted decision making is the assessment of a decision maker's risk/utility function associated with uncertain outcomes. The stochastic dominance concept enables the defining of preferences of one random entity (variable, vector, matrix, process, etc.) over another and therefore can be used to compare the different risks between decision alternatives. The concept of stochastic dominance is related to utility theory, which works with a class of utility functions where limited prior knowledge on a decision maker's utility function is assumed, which unfortunately results in very conservative models. Developing models, and the corresponding algorithms, that can systematically circumvent the challenges of traditional utility theory, especially when the decision is based on conflicting objectives and multiple random outcomes, is thus critically needed.

If successful, the results of this research will lead to the development of a new class of risk-adjusted decision models that incorporate risk in a more realistic way than currently existing techniques. Computationally efficient algorithms will be developed to solve these models.

Post-doctoral position at University of Houston

The Systems Optimization and Computing Laboratory at the University of Houston is seeking outstanding researcher for a post-doctoral position to begin August 16, 2011. The initial appointment is one year, which may be extended upon research performance and availability of funding for another year. Candidates should have expertise and research interest in optimization and healthcare systems (and preferably GPU-computation). The ideal candidate will have a Ph.D. in Operations Research, Industrial Engineering, Mathematics, or Computer Science. The successful candidate will work with Dr Gino Lim on developing optimization models and computational algorithms for very large-scale optimization problems in radiation treatment planning and/or homeland security. Candidates must have expertise in C/C++ and CPLEX (or Gurobi). Strong written and oral communication skills are must.

Other duties may include supervising Ph.D. students and teaching undergraduate level courses. We collaborate with the University of Texas MD Anderson Cancer Center on various exciting research projects.

Please send an electronic application to: ginolim@uh.edu. Application must include a curriculum vitae and a list of three professional references. Applications will be considered on a continuing basis beginning June 8, 2011 until the position is filled.

PostDoc Position in Wind and Solar Energy Siting Optimization

Advisers

MacDonald, Alexander E

Description

This opportunity involves assisting with an ongoing research project to optimize the siting of wind and solar energy projects using high-resolution numerical weather model data over multiple years. The literature contains work on relatively low penetration wind and/or solar power integration into the US grid. However, analysis and optimization of high penetration levels of renewable energy in the United States is lacking. This research has the potential to lead to multiple journal publications. In addition to the physical parameters of wind and solar radiation, this study will also optimize based on project economics. Computational work for this project is being done using a NOAA owned supercomputer, and the Associate will be working at the NOAA Earth System Research Laboratory in Boulder, CO. This project spans several disciplines and technical areas: mathematical optimization, coding (Python, FORTRAN, Linux), supercomputing, meteorology, renewable energy generation, electricity transmission, large-scale energy storage, GIS, and project finance/economics. Applicants are not expected to be expert, or even experienced in all of these areas, but having academic training or practical experience in many of these areas will be beneficial to the project and increase likelihood of success.

Reference
MacDonald AE: Monthly Weather Review 137: 1422, 2009

Keywords:

Optimization; Renewable energy; Meteorology; Numerical weather model;

NSF Award: Approximate Augmented Lagrangians, First-Order and Parallel Optimization Methods, with Applications to Stochastic Programming

Recent NSF Award funds a project lead by Jonathan Eckstein . The introduction of the project is stated as below

Continuous optimization is a mathematical discipline with extensive applications in engineering design and business/logistical planning. Its currently most common solution techniques are difficult to adapt to newly evolving computer architectures comprising dozens to thousands of processing elements working in parallel. Combining several existing techniques with some recent results of the principal investigator, this project explores a means of solving continuous optimization problems that should adapt more readily to parallel serial implementation, test it extensively, and address some computer architectures than present standard solvers, allowing the architectures' full power to be brought to bear on large, time-consuming problems. Without such new solution approaches, solution of critical design and planning problems may not benefit from most of the advances in computing power anticipated for the next decade. The project will also involve cooperative work with the Brazilian research community. The technical approach is to capitalize on recent advances in augmented Lagrangian and conjugate gradient algorithms to produce a new kind of modular parallel continuous constrained optimization solver. The solver consists of a classical augmented Lagrangian outer loop, with subproblems solved by the a state-of-the art box-constrained conjugate gradient method terminated by a recently developed relative error criterion. The research consists of three stages: the goal of stage one is to create an object-oriented, modular theoretical issues. Stage two aims to evolve the stage-one substrate into a parallel solver for which the user explicitly specifies how to map the problem structure to multiple processing elements. Stage three's goal is to automate the structure detection and mapping process. Stages two and three will use stochastic programming problems as test cases.

Google C++ Style Guide

Progress Reports: Interpreting the Node Log

As we explained earlier, when ILOG CPLEX optimizes mixed integer programs, it builds a tree with the linear relaxation of the original MIP at the root and subproblems to optimize at the nodes of the tree. ILOG CPLEX reports its progress in optimizing the original problem in a node log file as it traverses this tree.

You control how information in the log file is recorded and displayed, through two ILOG CPLEX parameters. The MIPDisplay parameter controls the general nature of the output that goes to the node log, as follows:

Table 8.11 Values of the MIP Display Parameter

Value	Effect
0	no display
1	display integer feasible solutions
2	display nodes under mip interval control
3	same as 2, but add information on node cuts
4	same as 3, but add LP display for root node
5	same as 3, but add LP display for all nodes

The MIPInterval parameter controls how frequently node log lines are printed. Its default is 100 and can be set to any positive integer value. A line is recorded in the node log for every node with an integer solution if the MIPDisplay parameter is set to 1 or higher, and also for any node whose number is a multiple of the MIPInterval value if the MIPDisplay is set to 2 or higher.

Here is an example of a log file from the Interactive Optimizer, where the MIPInterval parameter has been set to 10:

Tried aggregator 1 time. No MIP presolve or aggregator reductions. Presolve time = 0.00 sec. Root relaxation solution time = 0.00 sec Objective is integral. Nodes Cuts/ Node Left Objective IInf Best Integer Best Node ItCnt Gap 0 0 4.0000 6 4.0000 12 * 4 2 5.0000 0 5.0000 4.0000 17 20.00% 10 1 cutoff 5.0000 4.0000 31 20.00% Integer optimal solution: Objective = 5.0000000000e+000 Solution time = 0.02 sec. Iterations = 41 Nodes = 13

In that example, ILOG CPLEX found the optimal objective function value of 5.0 after exploring 13 nodes and performing 41 (dual simplex) iterations, and ILOG CPLEX found an optimal integer solution at node 4. The MIP interval parameter was set at 10, so every tenth node was logged, in addition to the node where an integer solution was found.

As you can see in that example, ILOG CPLEX logs an asterisk (*) in the left-most column for any node where it finds an integer-feasible solution. In the next column, it logs the node number. It next logs the number of nodes left to explore.

In the next column, ILOG CPLEX either records the objective value at the node or a reason to fathom the node. (A node is fathomed if the solution of a subproblem at the node is infeasible; or if the value of objective function at the node is worse than the cutoff value for branch & cut; or if the node supplies an integer solution.)

In the column labeled IInf, ILOG CPLEX records the number of integer-infeasible variables and special ordered sets. If no solution has been found, the next column is left blank; otherwise, it records the best integer solution found so far.

The column labeled Cuts/Best Node records the best objective function value of all the unexplored nodes. If the word Cuts appears in this column, it means various cuts were generated; if a particular name of a cut appears, then only that kind of cut was generated.

The column labeled ItCnt records the cumulative iteration count of the algorithm solving the subproblems. Until a solution has been found, the column labeled Gap is blank. If a solution has been found, the relative gap value is printed when it is less than 999.99; otherwise, hyphens are printed. The gap is computed as abs(best integer - best node)/(1e-10 + abs(best integer)). Consequently, the printed gap value may not always move smoothly. In particular, there may be sharp improvements whenever a new best integer solution is found.

ILOG CPLEX also logs its addition of cuts to a model. Here is an example of a node log file from a problem where ILOG CPLEX added several cover cuts.

MIP Presolve eliminated 0 rows and 1 columns. MIP Presolve modified 12 coefficients. Reduced MIP has 15 rows, 32 columns, and 97 nonzeros. Presolve time = 0.00 sec. Nodes Cuts/ Node Left Objective IInf Best Integer Best Node ItCnt Gap 0 0 2819.3574 7 2819.3574 35 2881.8340 8 Covers: 4 44 2881.8340 12 Covers: 3 48 * 7 6 3089.0000 0 3089.0000 2904.0815 62 5.99% Cover cuts applied: 30 Integer optimal solution: Objective = 3.0890000000e+003 Solution time = 0.10 sec. Iterations = 192 Nodes = 44

ILOG CPLEX also logs the number of clique inequalities in the clique table at the beginning of optimization. Cuts generated at intermediate nodes are not logged individually unless they happen to be generated at a node logged for other reasons. ILOG CPLEX logs the number of applied cuts of all classes at the end.

ILOG CPLEX also indicates, in the node log file, each instance of a successful application of the node heuristic. The following example shows a node log file for a problem where the heuristic found a solution at node 0. The + denotes a node generated by the heuristic.

Nodes Cuts/ Node Left Objective IInf Best Integer Best Node ItCnt Gap 0 0 403.8465 640 403.8465 4037 405.2839 609 Cliques: 10 5208 405.2891 612 Cliques: 2 5288 Heuristic: feasible at 437.000, still looking Heuristic: feasible at 437.000, still looking Heuristic complete * 0+ 0 436.0000 0 436.0000 405.2891 5288 7.04%

Periodically, if the MIP display parameter is 2 or greater, ILOG CPLEX records the cumulative time spent since the beginning of the current MIP optimization and the amount of memory used by branch & cut. (By periodically, we mean that time and memory information appears either every 20 nodes or ten times the MIP display parameter, whichever is greater.) The following example shows you one line from a node log file indicating elapsed time and memory use.

Elapsed b&c time = 120.01 sec. (tree size = 0.09 MB)

The Interactive Optimizer prints an additional summary line in the log if optimization stops before it is complete. This summary line shows the best MIP bound, that is, the best objective value among all the remaining node subproblems. The following example shows you lines from a node log file where an integer solution has not yet been found, and the best remaining objective value is 2973.9912281.

Node limit, no integer solution. Current MIP best bound = 2.9739912281e+03 (gap is infinite) Solution time = 0.01 sec. Iterations = 68 Nodes = 7 (7)

Sample: Stating a MIP Problem presents a typical MIP problem. Here is the node log file for that problem with the default setting of the MIP display parameter:

Tried aggregator 1 time. Aggregator did 1 substitutions. Reduced MIP has 2 rows, 3 columns, and 6 nonzeros. Presolve time = 0.00 sec. Clique table:0 GUB, 0 GUBEQ, 0 two-covers, 0 probed ImplBd table: 0 bounds Root relaxation solution time = 0.00 sec. Nodes Cuts/ Node Left Objective IInf Best Integer Best Node ItCnt Gap 0 0 125.2083 1 125.2083 3 * 122.5000 0 122.5000 Cuts: 2 4 Mixed integer rounding cuts applied: 1 Integer optimal solution: Objective = 1.2250000000e+002 Solution time = 0.02 sec. Iterations = 4 Nodes = 0

These additional items appear only in the node log file (not on screen):

• Variable records the name of the variable where ILOG CPLEX branched to create this node. If the branch was due to a special ordered set, the name listed here will be the right-most variable in the left subset.
• B indicates the branching direction:
  • D means the variables was restricted to a lower value;
  • U means the variable was restricted to a higher value;
  • L means the left subset of the special ordered set was restricted to 0 (zero);
  • R means the right subset of the special ordered set was restricted to 0 (zero).
• Parent indicates the node number of the parent.
• Depth indicates the depth of this node in the branch & cut tree.

---