A new natural-inspired continuous optimization approach

2018 ◽  
Vol 35 (3) ◽  
pp. 3267-3283 ◽  
Author(s):  
Mohamad Nabi Omidvar ◽  
Samad Nejatian ◽  
Hamid Parvin ◽  
Vahideh Rezaie
Keyword(s):  
Continuous Optimization ◽  
Optimization Approach

Computational Object-Wrapping Rope Nets

2022 ◽  
Vol 41 (1) ◽  
pp. 1-16
Author(s):  
Jian Liu ◽  
Shiqing Xin ◽  
Xifeng Gao ◽  
Kaihang Gao ◽  
Kai Xu ◽  
...  
Keyword(s):  
Continuous Optimization ◽  
Optimization Approach ◽  
Construction Process ◽  
Topological Constraints ◽  
3D Objects ◽  
Physical Experiments ◽  
Discrete Phase ◽  
Anchor Points ◽  
3D Shapes ◽  
Easy Operation

Wrapping objects using ropes is a common practice in our daily life. However, it is difficult to design and tie ropes on a 3D object with complex topology and geometry features while ensuring wrapping security and easy operation. In this article, we propose to compute a rope net that can tightly wrap around various 3D shapes. Our computed rope net not only immobilizes the object but also maintains the load balance during lifting. Based on the key observation that if every knot of the net has four adjacent curve edges, then only a single rope is needed to construct the entire net. We reformulate the rope net computation problem into a constrained curve network optimization. We propose a discrete-continuous optimization approach, where the topological constraints are satisfied in the discrete phase and the geometrical goals are achieved in the continuous stage. We also develop a hoist planning to pick anchor points so that the rope net equally distributes the load during hoisting. Furthermore, we simulate the wrapping process and use it to guide the physical rope net construction process. We demonstrate the effectiveness of our method on 3D objects with varying geometric and topological complexity. In addition, we conduct physical experiments to demonstrate the practicability of our method.

scholarly journals Deformable Registration with Discontinuity Preservation using Multi-Scale MRF

2014 ◽  
Author(s):  
Dohyung Seo ◽  
Jeroen Van Baar
Keyword(s):  
Discrete Optimization ◽  
Medical Image ◽  
Optimization Problem ◽  
Continuous Optimization ◽  
Deformable Registration ◽  
Optimization Approach ◽  
Smoothness Assumption ◽  
Breathing Motion ◽  
Multi Scale ◽  
3D Data

Deformable (2D or 3D) medical image registration is a challenging problem. Existing approaches assume that the underlying deformation is smooth. This smoothness assumption allows for solving the deformable registration at a coarse resolution and interpolate for finer resolutions. However, sliding of organs and breathing motion, exhibit discontinuities. We propose a discrete optimization approach to preserve these discontinuities. Solving continuous deformations using discrete optimization requires a fine distribution of the discrete labels. Coupled with the typical size of medical image datasets, this poses challenges to compute solutions efficiently. In this paper we present a practical, multi-scale formulation. We describe how discontinuities can be preserved, and how the optimization problem is solved. Results on synthetic 2D, and real 3D data show that we can well approximate the smoothness of continuous optimization, while accurately maintaining discontinuities.

scholarly journals Extracting active modules from multilayer PPI network: a continuous optimization approach

2017 ◽  
Author(s):  
Dong Li ◽  
Zexuan Zhu ◽  
Zhisong Pan ◽  
Guyu Hu ◽  
Shan He
Keyword(s):  
Network Topology ◽  
Cellular Response ◽  
Continuous Optimization ◽  
Single Layer ◽  
Optimization Approach ◽  
Identification Algorithm ◽  
Topological Properties ◽  
Ppi Network ◽  
Ppi Networks ◽  
Signaling Mechanisms

AbstractActive modules identification has received much attention due to its ability to reveal regulatory and signaling mechanisms of a given cellular response. Most existing algorithms identify active modules by extracting connected nodes with high activity scores from a graph. These algorithms do not consider other topological properties such as community structure, which may correspond to functional units. In this paper, we propose an active module identification algorithm based on a novel objective function, which considers both and network topology and nodes activity. This objective is formulated as a constrained quadratic programming problem, which is convex and can be solved by iterative methods. Furthermore, the framework is extended to the multilayer dynamic PPI networks. Empirical results on the single layer and multilayer PPI networks show the effectiveness of proposed algorithms.Availability: The package and code for reproducing all results and figures are available at https://github.com/fairmiracle/ModuleExtraction.

scholarly journals Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach

2016 ◽  
Author(s):  
Dong Li ◽  
Shan He
Keyword(s):  
Biological Networks ◽  
Structural Information ◽  
Natural Extension ◽  
Synthetic Data ◽  
Continuous Optimization ◽  
Single Layer ◽  
Supplementary Information ◽  
Optimization Approach ◽  
Unified Framework ◽  
Real World Data

AbstractMotivationSearching for active connected subgraphs in biological networks has shown important to identifying functional modules. Most existing active modules identification methods need both network structural information and gene activity measures, typically requiring prior knowledge database and high-throughput data. As a pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Searching for modules on WGCN thus has potential values. While traditional clustering based modules detection on WGCN method covers all genes, unavoidable introducing many uninformative ones when annotating modules. We need to find more accurate part of them.ResultsWe propose a fine-grained method to identify active modules on the multi-layer weighted (co-expression gene) network, based on a continuous optimization approach (AMOUNTAIN). The multilayer network are also considered under the unified framework, as a natural extension to single layer network case. The effectiveness is validated on both synthetic data and real-world data. And the software is provided as a user-friendly R package.AvailabilityAvailable at https://github.com/fairmiracle/[email protected] informationSupplementary data are available at Bioin-formatics online.

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