OptMap: Using Dense Maps for Visualizing Multidimensional Optimization Problems

Author(s)

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

• Document type: Journal article
• Source: 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.

Abstract

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)

• FAPESP's process: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/25835-9 - Understanding images and deep learning models
• Grantee: Nina Sumiko Tomita Hirata
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE