What is he best known for?
The fields of study he is best known for:
- Algorithm
- Mathematical optimization
- Linear programming
His primary scientific interests are in Discrete mathematics, Algorithm, Combinatorics, Mathematical optimization and Operations research.
His research integrates issues of Approximation algorithm and Knapsack problem in his study of Discrete mathematics.
When carried out as part of a general Algorithm research project, his work on Heuristic and Dynamic programming is frequently linked to work in Auxiliary memory, therefore connecting diverse disciplines of study.
Many of his research projects under Combinatorics are closely connected to Upper and lower bounds with Upper and lower bounds, tying the diverse disciplines of science together.
His work on Heuristics and Column generation as part of general Mathematical optimization research is frequently linked to Track, bridging the gap between disciplines.
His Operations research research is multidisciplinary, relying on both Scheduling, Crew scheduling and Transport engineering.
His most cited work include:
- Modeling and Solving the Train Timetabling Problem (368 citations)
- A Heuristic Method for the Set Covering Problem (359 citations)
- Algorithms for the Set Covering Problem (316 citations)
What are the main themes of his work throughout his whole career to date?
Alberto Caprara mainly investigates Mathematical optimization, Combinatorics, Discrete mathematics, Algorithm and Linear programming.
His work in Integer programming, Column generation, Lagrangian relaxation, Heuristic and Branch and bound is related to Mathematical optimization.
In general Integer programming study, his work on Cutting-plane method often relates to the realm of Train, thereby connecting several areas of interest.
His Combinatorics research focuses on Bin packing problem and how it relates to Packing problems.
The concepts of his Discrete mathematics study are interwoven with issues in Subset sum problem, Continuous knapsack problem, Knapsack problem and Set packing.
His study of Sorting is a part of Algorithm.
He most often published in these fields:
- Mathematical optimization (38.97%)
- Combinatorics (36.03%)
- Discrete mathematics (27.21%)
What were the highlights of his more recent work (between 2011-2019)?
- Mathematical optimization (38.97%)
- Knapsack problem (16.91%)
- Discrete mathematics (27.21%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Mathematical optimization, Knapsack problem, Discrete mathematics, Algorithm and Assignment problem.
In the subject of general Mathematical optimization, his work in Column generation, Integer programming, Lagrangian relaxation and Heuristic is often linked to Upper and lower bounds, thereby combining diverse domains of study.
His work carried out in the field of Integer programming brings together such families of science as Benders' decomposition, Complex system and Heuristic.
His research ties Combinatorics and Discrete mathematics together.
Alberto Caprara studies Linear programming which is a part of Algorithm.
His Assignment problem research integrates issues from Bounding overwatch and Operations research.
Between 2011 and 2019, his most popular works were:
- A Lagrangian Heuristic for Robustness, with an Application to Train Timetabling (69 citations)
- Railway Rolling Stock Planning: Robustness Against Large Disruptions (52 citations)
- An effective branch-and-bound algorithm for convex quadratic integer programming (43 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Algorithm
- Linear programming
Alberto Caprara spends much of his time researching Mathematical optimization, Knapsack problem, Continuous knapsack problem, Cutting stock problem and Time complexity.
In the field of Mathematical optimization, his study on Optimization problem, Stochastic programming and Iterative method overlaps with subjects such as Stock and Total cost.
The various areas that he examines in his Optimization problem study include Linear programming, Heuristic, Benders' decomposition and Integer programming.
His work on Change-making problem as part of general Knapsack problem study is frequently connected to Stackelberg competition, Interdiction and Simple, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The Continuous knapsack problem study combines topics in areas such as Computational complexity theory, Polynomial hierarchy, Polynomial-time approximation scheme and Combinatorics.
The subject of his Time complexity research is within the realm of Discrete mathematics.
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