OptMap: Using Dense Maps for Visualizing Multidimensional Optimization Problems

Autor(es)

Espadoto, Mateus ; Rodrigues, Francisco C. M. ; Hirata, Nina S. T. ; Telea, Alexandru C. ; Hurter, C ; Purchase, H ; Braz, J ; Bouatouch, K

• Tipo de documento: Artigo Científico
• Fonte: VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP; v. N/A, p. 10-pg., 2021-01-01.

Resumo

Operations Research is a very important discipline in many industries, and although there were many developments since its inception, to our knowledge there are no visualization tools focused on helping users understand the decision variables' domain space and its constraints for problems with more than two input dimensions. In this paper, we propose OptMap, a technique that enables the visual exploration of optimization problems using a two-dimensional dense map, regardless of the number of variables and constraints in the problem and for any kind of single-valued objective function. We show the technique in action for several optimization problems of different types, such as linear, nonlinear and integer, constrained and unconstrained problems. (AU)

• Processo FAPESP: 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 17/25835-9 - Interpretação de imagens e de modelos de aprendizado profundos
• Beneficiário: Nina Sumiko Tomita Hirata
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE