EDBT 2026 Demo / reviewers in the wild / expert
Marko Milenkovic
dblp:346/5566
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Computational geometry · 33% Automated reasoning and model checking · 33% Mathematical optimization · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization › combinatorial optimization
local search |
1.0 | 1 | 2026 | ETH Flippers Approach to Parallel Reconfiguration of Triangulations: SAT Formulation and Heuristics (CG Challenge) · SoCG 2026 |
Automated reasoning and model checking › satisfiability
SAT encoding |
1.0 | 1 | 2026 | ETH Flippers Approach to Parallel Reconfiguration of Triangulations: SAT Formulation and Heuristics (CG Challenge) · SoCG 2026 |
Computational geometry
triangulation |
1.0 | 1 | 2026 | ETH Flippers Approach to Parallel Reconfiguration of Triangulations: SAT Formulation and Heuristics (CG Challenge) · SoCG 2026 |
Methods — techniques the papers use, named apart from their topics
greedy local search · 1.0edge coloring · 1.0SAT solving · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | ETH Flippers Approach to Parallel Reconfiguration of Triangulations: SAT Formulation and Heuristics (CG Challenge)abstractWe describe the algorithms used by the ETH Flippers team in the CG:SHOP 2026 Challenge. Each instance consists of a set of triangulations on a common point set, and the objective is to find a central triangulation that minimizes the total parallel flip distance to the input set. Our strategy combines an exact solver for small and medium-sized instances with a suite of heuristics for larger instances. For the exact approach, we formulate the problem as a SAT instance with XOR clauses to model edge transitions across multiple rounds, further optimized by lower bounds derived from exact pairwise distances. For larger instances, we use a greedy local search and edge-coloring techniques to identify maximal sets of independent flips. Our approach ranked second overall and first in the junior category, computing provably optimal solutions for 186 out of 250 instances. Lorenzo Battini, Marko Milenkovic |
SoCG | 2 |