ESOPO: an Environment for Solving Optimization Problems Online

1. ESOPO: an Environment for Solving Optimization Problems Online M. D’Apuzzo*, M.L. De Cesare**, M.R. Maddalena**, M. Marino**, G. Toraldo** Collaborators: S. Cafieri*, V. De Simone*, D. di Serafino*, E. Sacchettino* * Second University of Naples **University of Naples Federico II

2. http://www.firb_lsno.unina.it

4. Overview • ESOPO aims and structure overview • Relevant features of ESOPO • Perspectives and future enhancements

5. Early motivation for ESOPO to provide a unifying framework containing the optimization software produced by people working in the MIUR FIRB project, in order to interact in the software development, testing and evaluation processes • Several issues • Shared software classification criteria • Common linear algebra kernels • Common optimization subproblems • Standard software documentation • Shared test problems • Similar input formats

6. Current ESOPO’s ambition to be a web-based environment forsolvingoptimization problems and for evaluating and comparing the performance of optimization software Several issues Software integration procedure Robustness and reliability Preprocessing and presolving stages Drivers to the solvers for using common problem modeling languages Minimal input effort Testing process

7. Current ESOPO’s ambition to be a web-based environment forsolvingoptimization problems and for evaluating and comparing the performance of optimization software Several issues Interactive procedure for solving a problem Interactive choice of a solver Dynamic interfaces for using the solver Automatic selection of test problems based on the type of considered instance

8. ESOPO project • MAIN ACTIONS • collect, integrate and make available the optimization software produced in the MIUR-FIRB Project, toghether with some well established software (Lancelot, KNITRO, Mosek, ...) • supply the solvers with drivers for the most common problem modeling languages and with graphical interfaces for a friendly usage • provide suitable collections of test problems and up-to-date tools for evaluating and comparing optimization software

9. problem user provided or selected from collections set of problems user provided or selected from collections Main ESOPO abilities ESOPO: SOLVE solution ESOPO: PERFORMANCE EVALUATION performace evaluation profiles

10. ..... ..... ESOPO architectureclient-server design • Solvers • Drivers ESOPO Solvers job execution • Users database • Software and Problems database • Interfaces for choosing solvers and for submitting problems • Tools for job queuing results answer ESOPO Server request Clients (browsers)

11. Relevant features • software integration process • interactive procedure for choosing a solver and for solving a problem • close integration of solvers and test problems • integration of the solving tools with the benchmarking tools

12. Relevant features Integration and management of the Software (authors are only request to submit the code!) Step 1: Classification into ESOPO Example: SDBOX (solves general bound constrained nonlinear optimization problems using a derivative-free method) OP: local; OF: general; CO: bounds; DR: none; CVX: no; STR: dense

14. Relevant features Integration and management of the Software Step 2: Development of drivers to the solver • Make its use through dynamic web pages easier • Provide interfaces to AMPL and SIF modeling languages • Reduce as much as possible the number of input parameters • Perform the testing process • Supply some extra features to the solver

15. Relevant features Interactive procedure for solving a problem (problem oriented and independent of the computing engine) Step 1: Specification of the problem web interface that allows the user to supply information about the problem to be solved

17. Relevant features Interactive procedure for solving a problem Step 2: Selection of a solver web interface that lists all solvers available for the problem

19. Relevant features Interactive procedure for solving a problem Step 3: Choice of the input format tailored interface for the selected solver (automatically generated) allowing the users to choose the input format among those accepted by the solver

21. Relevant features Interactive procedure for solving a problem Step 4: Submission of the problem specific interface consistent with the user’s choice for the input format (automatically generated) that allows the user to provide the problem data and the values for the input parameters

23. Relevant features Close integration of solvers and test problems A set of test problems that the software is able to solve is automatically selected

25. Execution report *************************************************************************** * * * Output report from ESOPO * * * *************************************************************************** SOLVER: SDBOX PROBLEM: BIGGSB1 from CUTEr collection VERSION: AMPL # Source: # M. Batholomew-Biggs and F.G. Hernandez, # "Some improvements to the subroutine OPALQP for dealing with large # problems", # Numerical Optimization Centre, Hatfield, 1992. # SIF input: Ph Toint, April 1992. # classification QBR2-AN-V-V NVAR = 5000 INPUT PARAMETERS: TOL = 10e-6 - MAXITER = 1000000 RESULTS: NIT = 181158 NFEVAL = 544749 FVAL = 0.015003

26. Relevant features Interactive procedure for evaluating and comparing the performance of optimization software The solving and benchmarking stages are integrated in ESOPO

27. Performance evaluation report 10 7 8 11 1 9 12 4 2 5 6 3

28. ESOPO contents

29. Future developments • to add more solvers also in areas not currently covered • to improve the interaction between users and ESOPO • to provide other metrics for the performance evaluation