Design of algorithm studies methods and techniques used to develop efficient algorithms. The design of efficient algorithms is often a critical first step in solving problems in many areas. Depending on the model of computation or computer platform that is required for an application, one might draw on algorithmic research in specific subareas. Several CSE faculty members are involved in algorithmic research.
- Graph Algorithms (He, Ngo)
The research on graph algorithms has been a driving force in the field of design and analysis of algorithms. Many practical application problems can be modeled by graph problems. With recent developments in computer science (such as the Internet and networking), many new problems arise and create new research opportunities.
Since the computational power of a single processor is limited, large-scale problems require multiprocessor machines. The study of parallel algorithms and architectures is concerned with the design and implementation of efficient techniques and strategies to solve problems on machines in which multiple processors are connected.
Graph drawing deals with the problem of constructing two- and three-dimensional visualizations of graphs. A good visualization of a graph has several important properties, such as few edge-crossings, small area, good aspect-ratio, and few edge-bends. It has applications in practical areas such as Computer Graphics, VLSI design, Computer Animation etc. This field focuses on developing efficient algorithms for constructing good visualizations of graphs. It uses techniques in Graph algorithm and Computational Geometry.
Computational geometry is a discipline concerned with the design, analysis, and implementation of efficient algorithms for solving problems best described as a set geometric objects such as points, curves, surfaces, and polyhedra. Since many real world problems can be modeled as geometric objects in certain metric spaces (such as Euclidean spaces), this area has strong connection with many applied areas such as Medicine, Biology, Networking, Graphics, Robotics, and VLSI design. Exploiting geometric and combinatorial properties of these problems often leads to better quality and more efficient solutions.
Group testing dates back to World War II. Algorithms are derived to identify a set of “positives” in a large population of given items, assuming some mechanism exists that indicates if
a pool of items has at least one positive. Direct applications of group testing algorithms include DNA-library screening, multiple access control, and mining association rules. Fundamental problems in group testing relate to mathematical disciplines like combinatorics, extremal set theory and algebra, making research on group testing very fruitful.
Complexity theory is a mathematical discipline that classifies computational problems by relative difficulty and measures the computational resources needed to solve them. It explains why certain problems have no practical solutions and helps researchers anticipate the difficulties involved in solving problems of certain types. The classification is quantitative and investigates both the resources that are necessary to solve a problem called lower bounds for the problem and the resources currently known to be sufficient called upper bounds. In general, complexity theory deals with the quantitative laws of computation and reasoning. For this reason, complexity theory concerns issues and problems of direct interest to many other disciplines as well.
Alan Selman’s research is concerned with properties of complexity classes, with relationships between classes, and with identification of properties of problems that affect their computational complexity. He has focused on several areas:
- Average case complexity, which provides mechanisms for classification of computational problems according to average use of resources, rather than worst-case usage
- Complexity theoretic underpinnings of public-key cryptography, including research on promise problems and complexity of classes of partial functions
- Polynomial-time-bounded reducibilities
- P-selective sets, which are an important tool for studying reducibilities and function classes
- Self-reducibility, which tends to lower complexity
Ken Regan’s research focuses on the obstacles to proving non-trivial lower bounds in complexity theory. Motivated by the fact that virtually no super-linear, let alone super-polynomial, time lower bounds are known for practical problems, part of Regan’s work has developed the less-attended theory of linear-time classes. Regan has obtained super-linear lower bounds on time or circuit-size for some problems under certain restrictions. Currently, Regan is pursuing a mathematical approach to breaking barriers to proving super-polynomial lower bounds. Regan’s work includes:
- Linear time computation and super-linear lower bounds
- Polynomial ideals and algebraic geometry as tools for lower bounds
- Mathematical logic for analyzing provability and characterizing complexity classes
- Complexity-bounded measure theory
- Fixed-parameter complexity theory
Category: Computer Science