SYNASC 2020 : 22th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

Tracks    

Symbolic Computation

• computer algebra
• symbolic techniques applied to numerics
• hybrid symbolic and numeric algorithms
• numerics and symbolics for geometry
• programming with constraints, narrowing

Numerical Computing

• iterative approximation of fixed points
• solving systems of nonlinear equations
• numerical and symbolic algorithms for differential equations
• numerical and symbolic algorithms for optimization
• parallel algorithms for numerical computing
• scientific visualization and image processing

Logic and Programming

• automatic reasoning
• formal system verification
• formal verification and synthesis
• software quality assessment
• static analysis
• timing analysis

Artificial Intelligence

• intelligent systems for scientific computing
• recommender and expert systems for scientific computing
• scientific knowledge management
• agent-based complex systems modeling and development
• uncertain reasoning in scientific computing     
• computational intelligence
• soft computing
• machine learning
• deep learning
• data mining, text mining and web mining
• natural language processing
• computer vision
• intelligent hybrid systems

Distributed Computing

• parallel and distributed algorithms for clouds, GPUs, HPC, P2P 
• systems, autonomous systems. Work should focus on scheduling, scaling, 
• load balancing, networks, fault-tolerance, gossip algorithms, energy saving applications for parallel and distributed systems, including work 
• on cross disciplinary (scientific) applications for grids/clouds, web applications, workflow platforms, network measurement tools, programming environments
• architectures for parallel and distributed systems, including self-managing and autonomous systems, negotiation protocols, HPC on clouds, GPU processing, PaaS for (inter)cloud, brokering platforms, mobile computing modelling of parallel and distributed systems including models on resources and networks, semantic representation, negotiation, social networks,  trace management, simulators
• any other topic deemed relevant to the field

Advances in the Theory of Computing

• data structures and algorithms
• combinatorial optimization
• formal languages and combinatorics on words
• graph-theoretic and combinatorial methods in computer science
• algorithmic paradigms, including distributed, online,
• approximation, probabilistic, game-theoretic algorithms
• computational complexity theory, including structural complexity, boolean complexity, communication complexity, average-case complexity, derandomization and property testing
• logical approaches to complexity, including finite model theory
• algorithmic and computational learning theory
• aspects of computability theory, including computability in
• analysis and algorithmic information theory
• proof complexity
• computational social choice and game theory
• new computational paradigms: CNN computing, quantum,
• holographic and other non-standard approaches to computability
• randomized methods, random graphs, threshold phenomena and
• typical-case complexity
• automata theory and other formal models, particularly in
• relation to formal verification methods such as model
• checking and runtime verification
• applications of theory, including wireless and sensor networks,
• computational biology and computational economics
• experimental algorithmics

This list is not intended to be exhaustive.