Hierarchical optimization for the efficient parametrization of ODE models

AbstractMathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems. In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness, and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches.

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Hybrid Optimization Approach for Evaluating Photovoltaic Cell Parameters

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.373-375.1261 ◽

2013 ◽

Vol 373-375 ◽

pp. 1261-1264

Author(s):

Mei Ying Ye

Keyword(s):

Cell Model ◽

Computation Time ◽

Photovoltaic Cell ◽

Evaluation Procedure ◽

Model Parameters ◽

Optimization Approach ◽

Problem Space ◽

Local Optima ◽

Cell Parameters ◽

Intelligent Technique

A new hybrid intelligent technique is proposed to evaluate photovoltaic cell model parameters in this paper. The intelligent technique is based on a hybrid of genetic algorithm (GA) and LevenbergMarquardt algorithm (LMA). In the proposed hybrid intelligent technique, the GA firstly searches the entire problem space to get a set of roughly estimated solutions, i.e. near-optimal solutions. Then the LMA performs a local optima search in order to carry out further optimizations. An example has been used to demonstrate the evaluation procedure in order to test the performance of the proposed approach. The results show that the proposed technique has better performance than the GA approach in terms of the objective function value, the computation time and the reconstructedI-Vcurve shape.

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Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

Sensors ◽

10.3390/s21061955 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1955

Author(s):

Md Jubaer Hossain Pantho ◽

Pankaj Bhowmik ◽

Christophe Bobda

Keyword(s):

Power Consumption ◽

High Speed ◽

Image Sensor ◽

Machine Learning Algorithms ◽

Hierarchical Optimization ◽

Computation Method ◽

Optimization Approach ◽

Efficient Computation ◽

Feature Maps ◽

Dynamic Power

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities.

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Effect of the Foresight Horizon on Computation Time and Results Using a Regional Energy Systems Optimization Model

Energies ◽

10.3390/en14020495 ◽

2021 ◽

Vol 14 (2) ◽

pp. 495

Author(s):

Jessica Thomsen ◽

Noha Saad Hussein ◽

Arnold Dolderer ◽

Christoph Kost

Keyword(s):

Computation Time ◽

Energy Systems ◽

Energy System ◽

Variable Cost ◽

Optimization Approach ◽

Or Efficiency ◽

Electricity Grid ◽

Regional Energy ◽

Long Run ◽

Regional Energy Systems

Due to the high complexity of detailed sector-coupling models, a perfect foresight optimization approach reaches complexity levels that either requires a reduction of covered time-steps or very long run-times. To mitigate these issues, a myopic approach with limited foresight can be used. This paper examines the influence of the foresight horizon on local energy systems using the model DISTRICT. DISTRICT is characterized by its intersectoral approach to a regionally bound energy system with a connection to the superior electricity grid level. It is shown that with the advantage of a significantly reduced run-time, a limited foresight yields fairly similar results when the input parameters show a stable development. With unexpected, shock-like events, limited foresight shows more realistic results since it cannot foresee the sudden parameter changes. In general, the limited foresight approach tends to invest into generation technologies with low variable cost and avoids investing into demand reduction or efficiency with high upfront costs as it cannot compute the benefits over the time span necessary for full cost recovery. These aspects should be considered when choosing the foresight horizon.

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A Hierarchical Optimization Approach to Optimal Production Scheduling in an Industrial Continuous Olefin Polymerization Reactor

Macromolecular Reaction Engineering ◽

10.1002/mren.200800030 ◽

2009 ◽

Vol 3 (1) ◽

pp. 36-46 ◽

Cited By ~ 23

Author(s):

Christos Chatzidoukas ◽

Stratos Pistikopoulos ◽

Costas Kiparissides

Keyword(s):

Production Scheduling ◽

Olefin Polymerization ◽

Hierarchical Optimization ◽

Optimization Approach ◽

Optimal Production ◽

Polymerization Reactor

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Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

Geophysics ◽

10.1190/geo2016-0384.1 ◽

2018 ◽

Vol 83 (2) ◽

pp. V99-V113 ◽

Cited By ~ 7

Author(s):

Zhong-Xiao Li ◽

Zhen-Chun Li

Keyword(s):

Field Data ◽

Optimization Problem ◽

Computational Cost ◽

Computation Time ◽

Blind Separation ◽

L1 Norm ◽

Norm Minimization ◽

Iterative Shrinkage ◽

Thresholding Algorithm ◽

Iterative Shrinkage Thresholding

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

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Frequency optimization of laminated functionally graded carbon nanotube reinforced composite quadrilateral plates using smoothed FEM and evolution algorithm

Journal of Composite Materials ◽

10.1177/0021998317737831 ◽

2017 ◽

Vol 52 (14) ◽

pp. 1971-1986 ◽

Cited By ~ 7

Author(s):

T Vo-Duy ◽

T Truong-Thi ◽

V Ho-Huu ◽

T Nguyen-Thoi

Keyword(s):

Carbon Nanotube ◽

Optimization Problem ◽

Volume Fraction ◽

Differential Evolution Algorithm ◽

Composite Plates ◽

Functionally Graded ◽

Optimization Approach ◽

Reinforced Composite ◽

Design Variables ◽

Evolution Algorithm

The paper presents an efficient numerical optimization approach to deal with the optimization problem for maximizing the fundamental frequency of laminated functionally graded carbon nanotube-reinforced composite quadrilateral plates. The proposed approach is a combination of the cell-based smoothed discrete shear gap method (CS-DSG3) for analyzing the first natural frequency of the functionally graded carbon nanotube reinforced composite plates and a global optimization algorithm, namely adaptive elitist differential evolution algorithm (aeDE), for solving the optimization problem. The design variables are the carbon nanotube orientation in the layers and constrained in the range of integer numbers belonging to [−900 900]. Several numerical examples are presented to investigate optimum design of quadrilateral laminated functionally graded carbon nanotube reinforced composite plates with various parameters such as carbon nanotube distribution, carbon nanotube volume fraction, boundary condition and number of layers.

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A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

Processes ◽

10.3390/pr6080126 ◽

2018 ◽

Vol 6 (8) ◽

pp. 126 ◽

Cited By ~ 2

Author(s):

Lina Aboulmouna ◽

Shakti Gupta ◽

Mano Maurya ◽

Frank DeVilbiss ◽

Shankar Subramaniam ◽

...

Keyword(s):

Mammalian Cells ◽

Metabolic Regulation ◽

Metabolic Flux ◽

Optimal Parameter ◽

Gene Knockout ◽

Model Parameters ◽

Mouse Bone Marrow ◽

Optimization Approach ◽

Substrate Uptake ◽

Data Set

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation.

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Qualitative Knowledge and Manufacturing Considerations in Multidisciplinary Structural Optimization of Hybrid Material Structures

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.10.143 ◽

2006 ◽

Vol 10 ◽

pp. 143-152 ◽

Cited By ~ 4

Author(s):

Martin Huber ◽

Horst Baier

Keyword(s):

Hybrid Material ◽

Optimization Problem ◽

Fuzzy Rule ◽

Optimal Designs ◽

Design Parameters ◽

Optimization Approach ◽

Structural Level ◽

Design Problems ◽

Qualitative Knowledge ◽

Typical Design

An optimization approach is derived from typical design problems of hybrid material structures, which provides the engineer with optimal designs. Complex geometries, different materials and manufacturing aspects are handled as design parameters using a genetic algorithm. To take qualitative information into account, fuzzy rule based systems are utilized in order to consider all relevant aspects in the optimization problem. This paper shows results for optimization tasks on component and structural level.

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Disease control as an optimization problem

PLoS ONE ◽

10.1371/journal.pone.0257958 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257958

Author(s):

Miguel Navascués ◽

Costantino Budroni ◽

Yelena Guryanova

Keyword(s):

Disease Control ◽

Stochastic Models ◽

Optimization Problem ◽

Initial Conditions ◽

Optimization Theory ◽

Model Parameters ◽

Grid Search ◽

Brute Force ◽

Time Required ◽

Policy Optimization

In the context of epidemiology, policies for disease control are often devised through a mixture of intuition and brute-force, whereby the set of logically conceivable policies is narrowed down to a small family described by a few parameters, following which linearization or grid search is used to identify the optimal policy within the set. This scheme runs the risk of leaving out more complex (and perhaps counter-intuitive) policies for disease control that could tackle the disease more efficiently. In this article, we use techniques from convex optimization theory and machine learning to conduct optimizations over disease policies described by hundreds of parameters. In contrast to past approaches for policy optimization based on control theory, our framework can deal with arbitrary uncertainties on the initial conditions and model parameters controlling the spread of the disease, and stochastic models. In addition, our methods allow for optimization over policies which remain constant over weekly periods, specified by either continuous or discrete (e.g.: lockdown on/off) government measures. We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020).

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Hyper-optimized tensor network contraction

Quantum ◽

10.22331/q-2021-03-15-410 ◽

2021 ◽

Vol 5 ◽

pp. 410

Author(s):

Johnnie Gray ◽

Stefanos Kourtis

Keyword(s):

Computation Time ◽

Quantum Circuit ◽

Optimization Approach ◽

Many Body ◽

Tensor Networks ◽

Randomized Protocols ◽

Classical Simulation ◽

Tensor Network ◽

Speed Up ◽

Many Body Systems

Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a variety of benchmarks, including the random quantum circuit instances recently implemented on Google quantum chips. We find that the paths obtained can be very close to optimal, and often many orders or magnitude better than the most established approaches. As different underlying geometries suit different methods, we also introduce a hyper-optimization approach, where both the method applied and its algorithmic parameters are tuned during the path finding. The increase in quality of contraction schemes found has significant practical implications for the simulation of quantum many-body systems and particularly for the benchmarking of new quantum chips. Concretely, we estimate a speed-up of over 10,000× compared to the original expectation for the classical simulation of the Sycamore `supremacy' circuits.

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