Convegno Progetto FIRB LSNO – Capri 19/20 aprile 2007 1 ESOPO: an Environment for Solving...

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ESOPO: ESOPO: an Environment for Solving an Environment for Solving

Optimization Problems OnlineOptimization 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

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http://www.firb_lsno.unina.it

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Overview Overview

• ESOPO aims and structure overview

• Relevant features of ESOPO

• Perspectives and future enhancements

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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

Early motivation for ESOPOEarly motivation for ESOPO

Several issues•Shared software classification criteria•Common linear algebra kernels•Common optimization subproblems•Standard software documentation•Shared test problems•Similar input formats

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Current ESOPO’s ambitionCurrent ESOPO’s ambition

Several issuesSoftware integration procedure Robustness and reliability Preprocessing and presolving stages Drivers to the solvers for using common problem modeling languages Minimal input effort Testing process

to be a web-based environment for solving optimization problems and for evaluating and comparing the

performance of optimization software

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Current ESOPO’s ambitionCurrent ESOPO’s ambition

Several issuesInteractive 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

to be a web-based environment for solving optimization problems and for evaluating and comparing the

performance of optimization software

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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

ESOPO projectESOPO project

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Main ESOPO abilitiesMain ESOPO abilities

problemuser provided or

selected from collections

solution

set of problems

user provided orselected from collections

performaceevaluation

profiles

ESOPO: SOLVE

ESOPO: PERFORMANCE EVALUATION

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ESOPO Server

• Users database• Software and Problems database

• Interfaces for choosing solvers and for submitting problems

• Tools for job queuing

Clients (browsers)

• Solvers

• Drivers

request

answer

job execution

results

.....

.....

ESOPO architectureESOPO architectureclient-server designclient-server design

ESOPO Solvers

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Relevant featuresRelevant 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

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Relevant featuresRelevant featuresIntegration and management of theSoftware (authors are only request to submit thecode!)

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

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Relevant featuresRelevant featuresIntegration and management of theSoftware

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

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Relevant featuresRelevant featuresInteractive procedure for solving aproblem (problem oriented andindependent 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

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Relevant featuresRelevant featuresInteractive procedure for solving aproblem

Step 2: Selection of a solver web interface that lists all solvers available for

the problem

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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

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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

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Relevant featuresRelevant features Close integration of solvers and test problems

A set of test problems that the software is able to solve is automatically selected

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Execution reportExecution 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

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Relevant featuresRelevant features Interactive procedure for

evaluating and comparing the performance of optimization software

The solving and benchmarking stages are integrated in ESOPO

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Performance evaluation Performance evaluation reportreport

1 972 8 11

10

64 53 12

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ESOPO contents

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Future developmentsFuture 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

Convegno Progetto FIRB LSNO – Capri 19/20 aprile 2007 1 ESOPO: an Environment for Solving...

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