If the leader sets very high toll values , the followers will be discouraged from using the tolled arcs , so the profit obtained from that decision is not going to be convenient for the leader . A methodology to solve this problem using optimization software at the lower level and the metaheuristic Scatter Search at the upper level is proposed . The general architecture , main algorithms and implemented functions of an optimization software package for distribution network planning based on GIS are introduced . The principle of graphic interface implementation is emphasized . To evaluate the performance of the suggested algorithm , computational experiments are performed on well-known benchmark problems . Results of computational experiments show that the suggested algorithm gives better solutions than a commercial optimization software package and an existing algorithm . The DNFP model employs a bi-level , predictive modeling framework to manage the empirical effects of price elasticity and competition on sales and revenue , and to maximize the gross-margin of the demand group , while satisfying certain practical side-constraints . This model is then transformed by using the Reformulation – Linearization Technique in tandem with a sequential bound-tightening scheme to recover an MIP formulation having a relatively tight underlying linear programming relaxation , which can be effectively solved by any commercial optimization software package . We present sample computational results using randomly generated instances of DNFP having different constraint settings and price range restrictions that are representative of common business requirements , and analyze the empirical effects of certain key modeling parameters . Consequently , model calibration is a challenging task . In this study , SWMM was integrated with the Optimization Software Tool for Research Involving Computational Heuristics ( OSTRICH ) to perform single- and multi-objective automatic calibration . The newly developed OSTRICH-SWMM is an open-source tool with dozens of parallelized optimization algorithms . In this paper , we focus on a specific limited people mover system called personal rapid transit . We develop in this paper an optimization based software which could manage efficiently the empty vehicles movements within the personal rapid transit system . Within this context , a branch and bound algorithm is proposed and its efficiently is proved on a set of instances taken from the literature . But planning robot motion still remains a major problem in Robotics regardless of countless approaches proposed in multiple aspects trying to solve it . TrajOpt(Trajectory Optimizer)[23 ] is a state-of-art optimization-based software framework for planning robot motions . TrajOpt generates trajectory through constrained sequential convex optimization given several initial guesses , meaning TrajOpt would focus on finding the local minimum guided by an initial guess . The Uniform CPA examination will be offered as an adaptive multi-stage test ( MST ) beginning in April of 2004 . Examples of automated assembly using mixed integer programming solutions in Optimization Programming Language software ( OPL Studio 3.6.1 © ILOG Inc. ) illustrate MST design features and implementation strategies that can be generalized to other automated assembly problems . A compromise between the best possible combination of test content , and sustainability and security over time is afforded by linear programming techniques that make use of mixed integer optimization algorithms . We try to find the optimal sales quantity by maximizing profit , given as a nonlinear and non-convex objective function . For such complicated combinatorial optimization problems , exact algorithms and optimization commercial software such as LINGO are inefficient , especially on practical-size problems . In this paper we develop a hybrid genetic/simulated annealing algorithm to deal with this nonlinear problem . The usage of artificial intelligence algorithms instead of capturing the best images by recording the image several times and reduction of this time would be wise . For the first time in this study by using the classification method approach used in artificial neural networks , which is an artificial intelligence methods , the best image is obtained with optimization via software , not on the digital holographic system . The three important features that affected the system , while the holographic setup is constructing , are examined for 142 different situations and the results of these situations are classified into 5 different classes . Our implementation of MT - DNN is based on the PyTorch implementation of BERT . We used Adamax as our optimizer with a learning rate of 5e - 5 and a batch size of 32 . The maximum number of epochs was set to 5 . The region size is 7 and embedding size is 128 . We used adam as the optimizer with the initial learning rate 0.0001 and the batch size is set to 16 . As for the aggregation MLP , we set the size of the hidden layer as 2 times interaction feature length . Based on the characteristics of existing discrete cosine transform(DCT ) methods in literature , and results obtained from the experimental data of software optimization based on TMS320DM642 platform of Texas Instruments(TI),an optimized subroutine of 4x4 DCT for this platform is proposed . An optimized library function is obtained for special platform , which can be widely used in the implementation of the existing video standards , such as H264 . Abstract It is challenging to continuously maintain an optimal operation in microgrids , especially with intermittent renewable energy resources and dynamic load profile . Proper coordination between hardware-based control and software-based optimization is required to improve the overall system performance in these conditions . A novel methodology is introduced here to control voltage and current simultaneously in real time , as well as optimally reconfigure a DC microgrid system as the operating conditions change . First , we present a hardware-based solution where a solid-state device is used as an external journaling device to minimize the disk head seek . Second , we introduce a software-based optimization to allow asynchronously commit multiple journal transactions on the local back-end file system to minimize the penalty of serialization . Both our solutions are experimentally tested on Oak Ridge National Laboratory 's large-scale Spider storage system and ourmore » tests show that both methods nearly double the overall parallel write performance . The paper describes the main characteristics of the software optimization process . The quality characteristics of software applications are presented . A control system for LED screen based on MCU is studied in this paper . Using the VRS51L3074 ’s characteristic of high algorithm speed and SPI interface , a project which can be used to control super-scale LED screen is carried out by software optimizing and reasonable hardware designing . Combining the characteristic of multi-I/O interfaces , VRS51L3074 can not only control super-scale LED screen but also realize parallel operation to multi-pieces LED cell board , and then it takes on the capability of controlling super-scale abnormity LED screen . This paper briefly describes the VelociTI architecture , which belongs to VLTW architecture and the generic software developing flow . Some key software optimizing technologies for VelociTI DSP are introduced through typical examples where software optimizing is played an important role . The coding and decoding algorithm of recommendation G.722.2 and the TMS320C6416 are described briefly . The design of hardware and software in realtime implementation of G.722.2 vocoder based on TMS320C6416 are given , and the software design optimization is summarized . The experiment results of real-time implementation are also presented . The effective development of the software in the system determines the performance of the system 's real time processing capability . This paper introduces the software optimization flow and discusses the program optimization . ; ; In order to improve the efficiency of embedded software running on processor core , this paper proposes a hardware/software co-optimization approach for embedded software from the system point of view . The proposed stepwise methods aim at exploiting the structure and the resources of the processor as much as possible for software algorithm optimization . To achieve low memory usage and low frequency need for the same performance , this co-optimization approach was used to optimize embedded software of MP3 decoder based on a 16-bit fixed-point DSP core . We describe the design and implementation of Dynamo , a software dynamic optimization system that is capable of transparently improving the performance of a native instruction stream as it executes on the processor . The input native instruction stream to Dynamo can be dynamically generated ( by a JIT for example ) , or it can come from the execution of a statically compiled native binary . However , additional investment in hardware may significantly increase the system cost as well as daily power consumption . In this paper , we present evidence that well-selected software level optimization is capable of achieving comparable levels of performance without the cost and power consumption overhead caused by physically expanding the system . Specifically , we develop intelligent prefetching algorithms that are suitable for the unique workloads and user behaviors of the world 's largest satellite images distribution system managed by USGS EROS . This paper advances a constructive optimized scheme named global direct memory access ( GDMA ) for high-speed data interaction in the multi-core digital signal processor ( DSP ) systems . Furmore , we give important recommendations to actualize software programming optimization of GDMA from three aspects . This scheme is based on the key technique of enhanced direct memory access versions3 ( EDMA3 ) , quick direct memory access ( QDMA ) and internal direct memory access ( IDMA ) . This paper reports a comparison of six different integer programming formulations of the single-machine total tardiness problem . We created a set of especially difficult test problems and attempted to solve them with each of the formulations , using CPLEX software . We found that one formulation performs much more effectively than the others . It has also to limit the interference power at some measurement points defined by the primary user . We formulate the problem as a bi-level location problem and develop a matheuristic where a mixed integer program derived from the follower ’s problem is solved by CPLEX software . We prove that the follower ’s problem is NP-hard and the leader ’s problem is $ $ \varSigma _ 2^P$$Σ2P-hard . Extensive computational experiments using standard benchmark data ( CATS ) as well as newly generated more realistic test sets were conducted which showed the heuristic was able to provide optimal solutions for most test cases and is within 1 % from the optimums for the rest within very short times . Experiements comparing CPLEX 8.0 , the fastest current algorithm , showed the heuristic was able to provide equally godd or better solutions often requring less than 1 % of the time required by CPLEX 8.0 . Computational results are reported for seven instances based on real-life shipping networks . Solving the relaxed linear path flow model with a column generation algorithm outperforms solving the relaxed linear arc flow model with the CPLEX barrier solver even for very small instances . The proposed algorithm is able to solve instances with 234 ports , 16278 demands over 9 time periods in 34 minutes . Computational results are reported for seven instances based on real-life shipping networks . Solving the relaxed linear path-flow model with a column generation algorithm outperforms solving the relaxed linear arc-flow model with the CPLEX barrier solver even for very small instances . The proposed algorithm is able to solve instances with 234 ports , 16,278 demands over 9 time periods in 34 min . RHC is furthermore a well known way of balancing performance and computation requirements by iteratively solving path planning problems over a receding planning horizon , and adapt the length of that horizon to the computational resources available . The proposed approach is implemented in Matlab/Cplex and illustrated by a number of solved examples . Therefore we apply an iterative Receding Horizon Control ( RHC ) scheme where a number of smaller MILPs are solved over shorter planning horizons . The proposed approach is implemented in Matlab/Cplex and illustrated by a number of solved examples . In this paper , a high level heuristic procedure “ Tabu Search Algorithm ( TSA ) ” is proposed to provide good solutions to resource-constrained , deterministic activity duration project scheduling problems . We present the application results of the computational tabu search and OPL-CPLEX algorithm and compare them with that of earlier applicable researches along with a discussion about further research . Our computational results are presented , which establish the superiority of tabu search over the existing heuristic algorithms . Considering the similarity of the ILP with the Multidimensional Knapsack Problem , an Approximate Dynamic Programming ( ADP ) model is developed making use of a reduced ILP for the value function approximation . Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver ( B&B ) , and this is what can be recommended for use in yes – no Bloom filters . SIAM Journal of Optimization , submitted for publication ] is a dynamic constraint reduction method particularly effective on degenerate linear programs . It is able to achieve a reduction in CPU time of over a factor of three on some problems compared to the commercial implementation of the simplex method CPLEX . We present a number of further improvements and effective parameter choices for IPS . We study the effect of the various parameters used in our equations such as epsilon , granularity , earning rate and hop count . The problem is formulated as a multicommodity flow ( MCF ) problem and is solved using the optimization tool ILOG CPLEX . Our approach is scalable to large networks having a number of VPN ’s . In this model , we distinguish the difference of resource utilization between surgery and nonsurgery patients . Standard LP-solver CPLEX is applied to solve this model . The result obtained can not only work out the schedule efficiently , but also ensure the minimized total cost of patients and spread the surgeries to days of weeks , which avoids unbalanced and sometimes overwhelmed workloads for surgeons . K-Means clustering is used as a standard clustering technique and that has been further refined by nearest neighbourhood heuristic . For both these clustering , routing has been done using LINGO solver . Transportation cost has been calculated for the corresponding routing distance given by the solver . Establishing a multi-objective optimization function with the lowest system economic operation and pollution gas emission , considering the attribute differences and energy scheduling characteristics of different energy sources of cold , heat and electricity , and adopting different time scales to optimize the operation of the three energy sources of cold , heat and electricity , wherein the operation periods of electric energy , heat energy and cold energy are respectively 15 min , 30 min and 1 h ; The multi-objective problem is solved by standard linear weighting method . Finally , the mixed integer nonlinear programming model is calculated by LINGO solver . In the numerical simulation , the hotel summer front load parameters of Zhangjiakou , China are selected for simulation and compared with a single time scale system . Finally , we conduct two sets of numerical experiments and discuss the experimental results obtained by general-purpose LINGO solver , standard GA and GALS . The computational results show that the GALS achieves the better performance than LINGO solver and standard GA . The system uses the dataset which contains 200 documents of 20 different categories . The result represents that for Marathi text documents LINGO clustering algorithm is efficient . This is a highly practical problem that arises in the context of online reputation management , but it has not been directly addressed by any Natural Language Processing evaluation campaign or specific benchmarking corpus . We use a case study to illustrate the challenges of the task , and we provide an initial baseline based on the Lingo clustering algorithm . The data set consists of 107 Marathi documents of 3 different categories-Tourism , Health Programmes and Maharashtra festivals . The result shows that the performance of the LINGO clustering algorithm is good for categorizing the Marathi text documents . For the Marathi documents overall accuracy of the system is 91.10 % . From the Publisher : Finally there is a games book for Director developers ! Up until now , Lingo programmers have had to figure it out themselves while Java and C programmers have had dozens of games books to choose from . With Advanced Lingo for Games , the new book by Gary Rosenzweig , Lingo programmers can learn from an expert game developer who has created hundreds of games with Macromedia Director . In “ New Theory of Discriminant Analysis after R. Fisher ” ( 2016 ) , Shinmura had already explained LINGO Program1 in Chap . 2 . LINGO Program1 defines six MP-based LDFs such as Revised IP-OLDF ( RIP ) , Revised LP-OLDF , Revised IPLP-OLDF , H-SVM , two soft-margin SVMs such as SVM4 ( penalty c = 10000 ) and SVM1 ( penalty c = 1 ) . Everyone can evaluate six MP-based LDFs in the training samples at once . Section 10.4 introduces LINGO Program 3 of Method2 . Section 10.5 introduces the validation of Method2 by LINGO Program1 using common data . Section 10.6 is conclusion . We first derive the optimal solution for stations association . Then , we evaluate the effectiveness of the solution through the results obtained from Lingo optimization and NCTUns simulation packages . An empirical study of an automobile manufacturer , namely Geelran Motor and three sets of test problems of small , medium and large sizes were considered to show the applicability of the presented model and efficiency of the proposed meta-heuristic algorithms . Numerical results show that the LINGO 9.0 software package is capable of solving the empirical study and small problems . For medium and large problems , we propose two meta-heuristic algorithms , a genetic algorithm ( GA ) and a discrete version of the colonial competitive algorithm ( CCA ) . In connection with the real life situation , we have encountered a numerical example with the help of practical data to show the use of our proposed reduction method in two-stage STP . All the soft computations regarding the numerical problem solution are performed in Lingo 13.0 iterative platform . At the last , the overall contribution , iteration and managerial facts have been outlined following the result . F-Lingo is a chrome extension that works on top of the FutureLearn MOOC platform to support content-based language learning of domain-specific terminology for professional and academic purposes . Unfortunately the searching process of it is apt to be led to a local extremum because it considers only local information when choosing the new branch . An improving strategy is proposed in this paper , which combines a partial backtracking procedure with the scheme of filtered beam search . With this backtracking strategy , some temporarily pruned nodes will be reserved and reevaluated , hence better potential solutions can survive and the searching process can effectively avoid from being trapped into a local extremum . Motivated by the desire to estimate the number of order-preserving self maps of an ordered set , we compare three algorithms ( Simple Sampling [ 4 ] , Partial Backtracking [ 10 ] and Heuristic Sampling [ 1 ] ) which predict how many nodes of a search tree are visited . The comparison is for the original algorithms that apply to backtracking and for modifications that apply to forward checking . An adult ’s assistance in noticing relevant features of the problem was necessary for the more complex tasks that involved four complementary rules . The spatial scaffolding afforded by the RoboGan interface was well used by the children , as they consistently used partial backtracking strategies to improve their constructions , and employed modular construction strategies in the more complex tasks . The children ’s explanations following their construction usually capped at one rule , or two condition-action couples , one rule short of their final constructions .
If the leader sets very high toll values , the followers will be discouraged from using the tolled arcs , so the profit obtained from that decision is not going to be convenient for the leader . A methodology to solve this problem using optimization software at the lower level and the metaheuristic Scatter Search at the upper level is proposed .
The general architecture , main algorithms and implemented functions of an optimization software package for distribution network planning based on GIS are introduced . The principle of graphic interface implementation is emphasized .
To evaluate the performance of the suggested algorithm , computational experiments are performed on well-known benchmark problems . Results of computational experiments show that the suggested algorithm gives better solutions than a commercial optimization software package and an existing algorithm .
The DNFP model employs a bi-level , predictive modeling framework to manage the empirical effects of price elasticity and competition on sales and revenue , and to maximize the gross-margin of the demand group , while satisfying certain practical side-constraints . This model is then transformed by using the Reformulation – Linearization Technique in tandem with a sequential bound-tightening scheme to recover an MIP formulation having a relatively tight underlying linear programming relaxation , which can be effectively solved by any commercial optimization software package . We present sample computational results using randomly generated instances of DNFP having different constraint settings and price range restrictions that are representative of common business requirements , and analyze the empirical effects of certain key modeling parameters .
Consequently , model calibration is a challenging task . In this study , SWMM was integrated with the Optimization Software Tool for Research Involving Computational Heuristics ( OSTRICH ) to perform single- and multi-objective automatic calibration . The newly developed OSTRICH-SWMM is an open-source tool with dozens of parallelized optimization algorithms .
In this paper , we focus on a specific limited people mover system called personal rapid transit . We develop in this paper an optimization based software which could manage efficiently the empty vehicles movements within the personal rapid transit system . Within this context , a branch and bound algorithm is proposed and its efficiently is proved on a set of instances taken from the literature .
But planning robot motion still remains a major problem in Robotics regardless of countless approaches proposed in multiple aspects trying to solve it . TrajOpt(Trajectory Optimizer)[23 ] is a state-of-art optimization-based software framework for planning robot motions . TrajOpt generates trajectory through constrained sequential convex optimization given several initial guesses , meaning TrajOpt would focus on finding the local minimum guided by an initial guess .
The Uniform CPA examination will be offered as an adaptive multi-stage test ( MST ) beginning in April of 2004 . Examples of automated assembly using mixed integer programming solutions in Optimization Programming Language software ( OPL Studio 3.6.1 © ILOG Inc. ) illustrate MST design features and implementation strategies that can be generalized to other automated assembly problems . A compromise between the best possible combination of test content , and sustainability and security over time is afforded by linear programming techniques that make use of mixed integer optimization algorithms .
We try to find the optimal sales quantity by maximizing profit , given as a nonlinear and non-convex objective function . For such complicated combinatorial optimization problems , exact algorithms and optimization commercial software such as LINGO are inefficient , especially on practical-size problems . In this paper we develop a hybrid genetic/simulated annealing algorithm to deal with this nonlinear problem .
The usage of artificial intelligence algorithms instead of capturing the best images by recording the image several times and reduction of this time would be wise . For the first time in this study by using the classification method approach used in artificial neural networks , which is an artificial intelligence methods , the best image is obtained with optimization via software , not on the digital holographic system . The three important features that affected the system , while the holographic setup is constructing , are examined for 142 different situations and the results of these situations are classified into 5 different classes .
Our implementation of MT - DNN is based on the PyTorch implementation of BERT . We used Adamax as our optimizer with a learning rate of 5e - 5 and a batch size of 32 . The maximum number of epochs was set to 5 .
The region size is 7 and embedding size is 128 . We used adam as the optimizer with the initial learning rate 0.0001 and the batch size is set to 16 . As for the aggregation MLP , we set the size of the hidden layer as 2 times interaction feature length .
Based on the characteristics of existing discrete cosine transform(DCT ) methods in literature , and results obtained from the experimental data of software optimization based on TMS320DM642 platform of Texas Instruments(TI),an optimized subroutine of 4x4 DCT for this platform is proposed . An optimized library function is obtained for special platform , which can be widely used in the implementation of the existing video standards , such as H264 .
Abstract It is challenging to continuously maintain an optimal operation in microgrids , especially with intermittent renewable energy resources and dynamic load profile . Proper coordination between hardware-based control and software-based optimization is required to improve the overall system performance in these conditions . A novel methodology is introduced here to control voltage and current simultaneously in real time , as well as optimally reconfigure a DC microgrid system as the operating conditions change .
First , we present a hardware-based solution where a solid-state device is used as an external journaling device to minimize the disk head seek . Second , we introduce a software-based optimization to allow asynchronously commit multiple journal transactions on the local back-end file system to minimize the penalty of serialization . Both our solutions are experimentally tested on Oak Ridge National Laboratory 's large-scale Spider storage system and ourmore » tests show that both methods nearly double the overall parallel write performance .
The paper describes the main characteristics of the software optimization process . The quality characteristics of software applications are presented .
A control system for LED screen based on MCU is studied in this paper . Using the VRS51L3074 ’s characteristic of high algorithm speed and SPI interface , a project which can be used to control super-scale LED screen is carried out by software optimizing and reasonable hardware designing . Combining the characteristic of multi-I/O interfaces , VRS51L3074 can not only control super-scale LED screen but also realize parallel operation to multi-pieces LED cell board , and then it takes on the capability of controlling super-scale abnormity LED screen .
This paper briefly describes the VelociTI architecture , which belongs to VLTW architecture and the generic software developing flow . Some key software optimizing technologies for VelociTI DSP are introduced through typical examples where software optimizing is played an important role .
The coding and decoding algorithm of recommendation G.722.2 and the TMS320C6416 are described briefly . The design of hardware and software in realtime implementation of G.722.2 vocoder based on TMS320C6416 are given , and the software design optimization is summarized . The experiment results of real-time implementation are also presented .
The effective development of the software in the system determines the performance of the system 's real time processing capability . This paper introduces the software optimization flow and discusses the program optimization . ; ;
In order to improve the efficiency of embedded software running on processor core , this paper proposes a hardware/software co-optimization approach for embedded software from the system point of view . The proposed stepwise methods aim at exploiting the structure and the resources of the processor as much as possible for software algorithm optimization . To achieve low memory usage and low frequency need for the same performance , this co-optimization approach was used to optimize embedded software of MP3 decoder based on a 16-bit fixed-point DSP core .
We describe the design and implementation of Dynamo , a software dynamic optimization system that is capable of transparently improving the performance of a native instruction stream as it executes on the processor . The input native instruction stream to Dynamo can be dynamically generated ( by a JIT for example ) , or it can come from the execution of a statically compiled native binary .
However , additional investment in hardware may significantly increase the system cost as well as daily power consumption . In this paper , we present evidence that well-selected software level optimization is capable of achieving comparable levels of performance without the cost and power consumption overhead caused by physically expanding the system . Specifically , we develop intelligent prefetching algorithms that are suitable for the unique workloads and user behaviors of the world 's largest satellite images distribution system managed by USGS EROS .
This paper advances a constructive optimized scheme named global direct memory access ( GDMA ) for high-speed data interaction in the multi-core digital signal processor ( DSP ) systems . Furmore , we give important recommendations to actualize software programming optimization of GDMA from three aspects . This scheme is based on the key technique of enhanced direct memory access versions3 ( EDMA3 ) , quick direct memory access ( QDMA ) and internal direct memory access ( IDMA ) .
This paper reports a comparison of six different integer programming formulations of the single-machine total tardiness problem . We created a set of especially difficult test problems and attempted to solve them with each of the formulations , using CPLEX software . We found that one formulation performs much more effectively than the others .
It has also to limit the interference power at some measurement points defined by the primary user . We formulate the problem as a bi-level location problem and develop a matheuristic where a mixed integer program derived from the follower ’s problem is solved by CPLEX software . We prove that the follower ’s problem is NP-hard and the leader ’s problem is $ $ \varSigma _ 2^P$$Σ2P-hard .
Extensive computational experiments using standard benchmark data ( CATS ) as well as newly generated more realistic test sets were conducted which showed the heuristic was able to provide optimal solutions for most test cases and is within 1 % from the optimums for the rest within very short times . Experiements comparing CPLEX 8.0 , the fastest current algorithm , showed the heuristic was able to provide equally godd or better solutions often requring less than 1 % of the time required by CPLEX 8.0 .
Computational results are reported for seven instances based on real-life shipping networks . Solving the relaxed linear path flow model with a column generation algorithm outperforms solving the relaxed linear arc flow model with the CPLEX barrier solver even for very small instances . The proposed algorithm is able to solve instances with 234 ports , 16278 demands over 9 time periods in 34 minutes .
Computational results are reported for seven instances based on real-life shipping networks . Solving the relaxed linear path-flow model with a column generation algorithm outperforms solving the relaxed linear arc-flow model with the CPLEX barrier solver even for very small instances . The proposed algorithm is able to solve instances with 234 ports , 16,278 demands over 9 time periods in 34 min .
RHC is furthermore a well known way of balancing performance and computation requirements by iteratively solving path planning problems over a receding planning horizon , and adapt the length of that horizon to the computational resources available . The proposed approach is implemented in Matlab/Cplex and illustrated by a number of solved examples .
Therefore we apply an iterative Receding Horizon Control ( RHC ) scheme where a number of smaller MILPs are solved over shorter planning horizons . The proposed approach is implemented in Matlab/Cplex and illustrated by a number of solved examples .
In this paper , a high level heuristic procedure “ Tabu Search Algorithm ( TSA ) ” is proposed to provide good solutions to resource-constrained , deterministic activity duration project scheduling problems . We present the application results of the computational tabu search and OPL-CPLEX algorithm and compare them with that of earlier applicable researches along with a discussion about further research . Our computational results are presented , which establish the superiority of tabu search over the existing heuristic algorithms .
Considering the similarity of the ILP with the Multidimensional Knapsack Problem , an Approximate Dynamic Programming ( ADP ) model is developed making use of a reduced ILP for the value function approximation . Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver ( B&B ) , and this is what can be recommended for use in yes – no Bloom filters .
SIAM Journal of Optimization , submitted for publication ] is a dynamic constraint reduction method particularly effective on degenerate linear programs . It is able to achieve a reduction in CPU time of over a factor of three on some problems compared to the commercial implementation of the simplex method CPLEX . We present a number of further improvements and effective parameter choices for IPS .
We study the effect of the various parameters used in our equations such as epsilon , granularity , earning rate and hop count . The problem is formulated as a multicommodity flow ( MCF ) problem and is solved using the optimization tool ILOG CPLEX . Our approach is scalable to large networks having a number of VPN ’s .
In this model , we distinguish the difference of resource utilization between surgery and nonsurgery patients . Standard LP-solver CPLEX is applied to solve this model . The result obtained can not only work out the schedule efficiently , but also ensure the minimized total cost of patients and spread the surgeries to days of weeks , which avoids unbalanced and sometimes overwhelmed workloads for surgeons .
K-Means clustering is used as a standard clustering technique and that has been further refined by nearest neighbourhood heuristic . For both these clustering , routing has been done using LINGO solver . Transportation cost has been calculated for the corresponding routing distance given by the solver .
Establishing a multi-objective optimization function with the lowest system economic operation and pollution gas emission , considering the attribute differences and energy scheduling characteristics of different energy sources of cold , heat and electricity , and adopting different time scales to optimize the operation of the three energy sources of cold , heat and electricity , wherein the operation periods of electric energy , heat energy and cold energy are respectively 15 min , 30 min and 1 h ; The multi-objective problem is solved by standard linear weighting method . Finally , the mixed integer nonlinear programming model is calculated by LINGO solver . In the numerical simulation , the hotel summer front load parameters of Zhangjiakou , China are selected for simulation and compared with a single time scale system .
Finally , we conduct two sets of numerical experiments and discuss the experimental results obtained by general-purpose LINGO solver , standard GA and GALS . The computational results show that the GALS achieves the better performance than LINGO solver and standard GA .
The system uses the dataset which contains 200 documents of 20 different categories . The result represents that for Marathi text documents LINGO clustering algorithm is efficient .
This is a highly practical problem that arises in the context of online reputation management , but it has not been directly addressed by any Natural Language Processing evaluation campaign or specific benchmarking corpus . We use a case study to illustrate the challenges of the task , and we provide an initial baseline based on the Lingo clustering algorithm .
The data set consists of 107 Marathi documents of 3 different categories-Tourism , Health Programmes and Maharashtra festivals . The result shows that the performance of the LINGO clustering algorithm is good for categorizing the Marathi text documents . For the Marathi documents overall accuracy of the system is 91.10 % .
From the Publisher : Finally there is a games book for Director developers ! Up until now , Lingo programmers have had to figure it out themselves while Java and C programmers have had dozens of games books to choose from . With Advanced Lingo for Games , the new book by Gary Rosenzweig , Lingo programmers can learn from an expert game developer who has created hundreds of games with Macromedia Director .
In “ New Theory of Discriminant Analysis after R. Fisher ” ( 2016 ) , Shinmura had already explained LINGO Program1 in Chap . 2 . LINGO Program1 defines six MP-based LDFs such as Revised IP-OLDF ( RIP ) , Revised LP-OLDF , Revised IPLP-OLDF , H-SVM , two soft-margin SVMs such as SVM4 ( penalty c = 10000 ) and SVM1 ( penalty c = 1 ) . Everyone can evaluate six MP-based LDFs in the training samples at once .
Section 10.4 introduces LINGO Program 3 of Method2 . Section 10.5 introduces the validation of Method2 by LINGO Program1 using common data . Section 10.6 is conclusion .
We first derive the optimal solution for stations association . Then , we evaluate the effectiveness of the solution through the results obtained from Lingo optimization and NCTUns simulation packages .
An empirical study of an automobile manufacturer , namely Geelran Motor and three sets of test problems of small , medium and large sizes were considered to show the applicability of the presented model and efficiency of the proposed meta-heuristic algorithms . Numerical results show that the LINGO 9.0 software package is capable of solving the empirical study and small problems . For medium and large problems , we propose two meta-heuristic algorithms , a genetic algorithm ( GA ) and a discrete version of the colonial competitive algorithm ( CCA ) .
In connection with the real life situation , we have encountered a numerical example with the help of practical data to show the use of our proposed reduction method in two-stage STP . All the soft computations regarding the numerical problem solution are performed in Lingo 13.0 iterative platform . At the last , the overall contribution , iteration and managerial facts have been outlined following the result .
F-Lingo is a chrome extension that works on top of the FutureLearn MOOC platform to support content-based language learning of domain-specific terminology for professional and academic purposes .
Unfortunately the searching process of it is apt to be led to a local extremum because it considers only local information when choosing the new branch . An improving strategy is proposed in this paper , which combines a partial backtracking procedure with the scheme of filtered beam search . With this backtracking strategy , some temporarily pruned nodes will be reserved and reevaluated , hence better potential solutions can survive and the searching process can effectively avoid from being trapped into a local extremum .
Motivated by the desire to estimate the number of order-preserving self maps of an ordered set , we compare three algorithms ( Simple Sampling [ 4 ] , Partial Backtracking [ 10 ] and Heuristic Sampling [ 1 ] ) which predict how many nodes of a search tree are visited . The comparison is for the original algorithms that apply to backtracking and for modifications that apply to forward checking .
An adult ’s assistance in noticing relevant features of the problem was necessary for the more complex tasks that involved four complementary rules . The spatial scaffolding afforded by the RoboGan interface was well used by the children , as they consistently used partial backtracking strategies to improve their constructions , and employed modular construction strategies in the more complex tasks . The children ’s explanations following their construction usually capped at one rule , or two condition-action couples , one rule short of their final constructions .
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CISIM SIU ACL AAAI PLDI IPCCC ICRA MSWiM ISCBI LWMOOCS
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