Nonlinear programming algorithms

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Nonlinear Programming Algorithms for CAD Systems of Line Structure Routing

Nonlinear Programming Algorithms for CAD Systems of Line Structure Routing

problem is formalized as a system of linear inequalities [1,2] With the discrete representation of the unknown extremal ( the project line ) with sufficient for practical purposes accuracy we obtain the optimization problem with a few hundred variables and several thousands of constraints – inequalities. This problem is necessary to solve many times with the refinement and detail parameters of the mathematical model because of the interrelation with other design problems [2,4,5 ]. For that reason, the question arises about how to obtain solutions in reasonable time. The standard NLP algorithms was developed for linear constraints of general form. They require too much time in solving problems of large dimension and therefore unsuitable for use in CAD. Therefore still valid implementation of mathematical methods of nonlinear programming in more efficient algorithms and programs was taken into account the peculiarities of a particular task.
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Random Search as the Method of Nonlinear Programming. Algorithms of Random Search

Random Search as the Method of Nonlinear Programming. Algorithms of Random Search

The considered algorithm of random search to some extent uses training to select the next step. The learning and the search in this case are of a statistical nature. This is reflected in the fact that out of precisely defined number of tests at each step we choose only one, which determines the best direction. The relationship between the states of the system is very strong. The good one direction that has been found on previous step determines the subsequent search and the next step is not changed. Restructuring of the search system in a new direction takes place only if the objective function is not reduced or on the path of search system appears the restriction. The search system is too straightforward and is not optimal from the point of view of search costs. Probabilistic linkage between different states is absent. Learning takes place through trial and error in finding the most promising directions. Obviously, such direction can be found in other way by adjustment of probabilistic search characteristics and by introducing a self-learning of search system. Essential for such training is not only an increase or decrease in the objective function, but also an increase or decrease of each independent variable in the selected direction. Consider one of these algorithms.
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Nonlinear Programming

Nonlinear Programming

Conceptually, the simplest type of optimization is unconstrained. Powerful solution techniques have been developed for solving these problems, which are based primarily upon calculus, rather than upon algebra and pivoting, as in the simplex method. Because the linear-programming methods and unconstrained-optimization techniques are so efficient, both have been used as the point of departure for constructing more general- purpose nonlinear-programming algorithms. The previous sections have indicated some of the algorithms using the linear-programming-based approach. This section briefly indicates the nature of the unconstrained- optimization approaches by introducing algorithms for unconstrained maximization and showing how they might be used for problems with constraints.
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Semidefinite Programming. methods and algorithms for energy management

Semidefinite Programming. methods and algorithms for energy management

The technique for managing reserves is extended to other generation units which are subject to stock constraints, such as nuclear power plants and IOC. Regarding nuclear power, the stock constraint stems from the fact that at short-term, the fuel remaining in the reactor must last until the next outage, which is fixed. Regarding IOC, the number of days concerned by these contracts is finite and stipulated. As with the water reserve, the management of such stocks is made by computing value-in-use that allow to decide the use of these stocks in the present or in the future. This computation is actually the expression of a strategy, that depends on the considered time horizon and shall take uncertainties into account, while optimizing an economic criterion. For one stock, the problem is complex but tractable by dynamic programming. When, we have to define a strategy for the whole stocks, the problem becomes very challenging.
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Adaptive Algorithms for Sparse Nonlinear Channel Estimation

Adaptive Algorithms for Sparse Nonlinear Channel Estimation

Wireless communication channels are characterized by time varying multipath propagation effects. Quite often in prac- tice, several reflections reach the receiver at different time in- stances. These reflections arrive at the receiver with longer delay than the first group. Hence, the wireless channel is modeled by sparse fading rays and long zero samples and thus admits a sparse representation [1]. The sparseness char- acteristic is preserved when the PA representation is also de- scribed by a sparse model [2]. Recent experimental results reported in [2] indicate better performance if sparse nonlinear models are employed for the representation of PA. Moreover, the time-varying nature of the wireless channels suggest the use of adaptive algorithms that minimize transmission delays and take advantage of parameters sparsity. Thus, compressive sensing provides a promising framework for such develop- ments. Adaptive algorithms for sparse channel estimation are developed in [3, 4]. In [3] two different sparsity constraints are incorporated into the quadratic cost function of the LMS algorithm, to take into account the sparse channel coefficient vector. An ` 1 -regularized RLS type algorithm based on a low complexity Expectation-Maximization, is derived in [4].
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The Algorithms of Speech Recognition,Programming and Simulating in Matlab

The Algorithms of Speech Recognition,Programming and Simulating in Matlab

My aim of this thesis work is to Test the algorithms of speech recognition. I have programmed and simulated the designed systems for speech recognition’s Algorithms in MATLAB.Hence,There are two systems have been designed within this thesis.First One is based on the shape information of the cross-correlation plotting. The Second one is to use the Wiener Filter for realizing the speech recognition. The simulations of the systems which have been Programmed in MATLAB are accomplished by using the microphone for recording of the speaking words. After running the program in MATLAB, The system will ask people to record the words for three times.First and second recorded words are different words which will be used as the reference signals in the designed systems. The third recorded word is the same word as the one of the first two recorded words. After recording completed, the words will become the signals’ information that will be sampled and stored in MATLAB. Then MATLAB will be able to give the judgment that which word has been recorded at the third time comparing with the first two reference words according to the algorithms programmed in MATLAB. I invite different people from different Places with different utterence to test the designed systems. The results of simulations for both designed systems prove that both of the designed systems work well whenever the first two reference recordings and the third time recording are recorded from the same person. But the designed systems have the defects when the first two reference recordings and the third recording are recorded from the different people. However, if the testing environment is quiet enough and the speaker is the same person for recordings of three times, the successful probability of the speech recognition approach to 100 percent. Thus, the designed systems really work well for the speech recognition.
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Introduction to Programming in C++: Algorithms, Flowcharts and Pseudocode

Introduction to Programming in C++: Algorithms, Flowcharts and Pseudocode

A sequence of instructions is called an algorithm. Algorithms are a fundamental part of computing. If you study computing for many years you will study algorithms of frequently used processes. Books have been written on algorithms for such common activities as storing and ordering data. As most problems you get are unique, you will develop your own algorithms. However, you may find standard algorithms for those parts of your programs that do common activities.

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Some recent developments in nonlinear optimization algorithms

Some recent developments in nonlinear optimization algorithms

In order to compute or approach a local solution of problem (1), nonlinear optimization algorithms produce a sequence of iterates, { x k } say, that (hopefully) converges to such a solution, x ∗ say. As far as unconstrained optimization is concerned (i.e., when the feasible set is IR n ), important progress has been made since a while now in the solution of these problems, especially in the way to guarantee (both in theory and in practice) global convergence of the iterative process to a solution, i.e., convergence of the iterative process from any starting point. We will present in this paper the main ideas of the two major globalization techniques, called line-search method and trust-region method.
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A Glorious Literature on Linear Goal Programming Algorithms

A Glorious Literature on Linear Goal Programming Algorithms

However, debated weakness of LGP is that goal-programming approach, regardless of the weighting struc- tures (pre-emptive or Archimedean) and regardless of the goals (one-sided or two-sided), can lead to inferior (dominated or inefficient), suboptimal solutions which are not necessarily the “best” ones available to the deci- sion-maker. But, in Ignizio [4], it was proved that the optimal solution obtained by the lexicographic problem is Pareto optimal. Thus, the lexicographic method is always adopted as an additional optimization approach in methods that can only guarantee weak optimality by themselves. Evans [46] described GP problem as a tech- nique for finding that solution which minimizes the deviation over all feasible solutions; such a solution is called a best compromise solution and that under the assumption that more of each objective is preferred to less; a best compromise solution. Min and Storbeck [47] stated that GP is a technique not designed to find an “optimal point”, but to find an “acceptable range”, and advised that the dispute of GP dominance will continue unless the management scientist can accept goal programming’s satisficing principle and not being captivated by the prin- ciple of optimality. Miettinen [48] proved that GP technique yields nondominated solutions if the goal point is chosen in the feasible domain. However, in Goal programming, there is no method to determine if a solution is better than other. Nabendu and Manish [13] stated that the computational procedure in goal programming is to select a set of solutions which satisfies the constraints and providing a satisfactory goal, ranked in priority order since GP approach seeks satisficing solutions which come as close to the desired aspiration levels as possible. Antonio et al. [49] described Pareto dominance relation as the most commonly adopted method in multi objec- tive optimization to compare solutions which, instead of a single optimal solution, leads to a set of alternatives with different trade-offs among the objectives. Their solutions are called Pareto optimal solutions or non- domi- nated solutions. Although there are multiple Pareto optimal solutions, in practice, only one solution has to be se- lected for implementation.
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On hybrid split problem and its nonlinear algorithms

On hybrid split problem and its nonlinear algorithms

In last ten years or so, the problem (EP) has been generalized and improved to find a common element of the set of fixed points of a nonlinear operator and the set of solutions of the problem (EP). More precisely, many authors have studied the following problem (FTEP) (see, for instance, [–]):

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Nonlinear unmixing of hyperspectral images: Models and algorithms

Nonlinear unmixing of hyperspectral images: Models and algorithms

the endmember signatures and then estimate the abundance coefficients. For supervised unmixing, the N-FINDR algorithm [54] and its nonlinear geodesic-based counterpart [43] have been used to extract the endmembers from linear and nonlinear mixtures, respectively. Then, dedicated model-based strategies were used to recover the abundance fractions. The fully con- strained least square (FCLS) algorithm [35] was used for linear mixtures. Gradient-based algorithms (GBAs) were used for non- linear mixtures. The GBAs are detailed in [55], [34], and [9] for the PPNM, GBM, and FM, respectively. For comparison with supervised unmixing, and to evaluate the impact of having no pure pixels in these images, joint estimations of endmembers and abundances was implemented using the Markov chain Monte Carlo techniques detailed in [56] and [39] for the LMM and PPNM images, respectively. Finally, the model-free super- vised K-HYPE algorithm detailed in [48] was also coupled with the nonlinear EEA in [43]. The performance of these unmixing strategies has been evaluated in term of abundance estimation error measured by
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Adaptive Algorithms for Sparse Nonlinear Channel Estimation

Adaptive Algorithms for Sparse Nonlinear Channel Estimation

A third order channel model was used to test the derived algorithms. The wireless channel taps for the linear and cu- bic part were generated by sparse Rayleigh fading rays. All rays are assumed to fade at the same Doppler frequency of f D = 80Hz with sampling period T s = 0.8µs. The linear

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Core Techniques and Algorithms in Game Programming pdf

Core Techniques and Algorithms in Game Programming pdf

Many types of applications, and games in particular, need to hold heterogeneous collections of data together for different reasons. A game level can have sublevels (which in turn can have sublevels), potions, enemies (which can be composed, for example, as in a horse and rider approach), objects, and so on. The overall data structure can be best described as a part-whole hierarchy with each element being either a primitive or a composite, quite likely of different types. Having all data in a single structure makes traversal more intuitive, especially when we couple the composite with a spatial index that allows local queries such as "Which potions are in this room?" Thus, it would be great if programming languages offered some constructs that made the implementation of these complex collections easier. But most programming languages only support homogeneous arrays, so a higher-abstraction solution is needed. This is what the composite DP is all about: creating part-whole heterogeneous hierarchies where we can access primitives and composite objects using a standard interface. This way a single interface will make traversal easier, and each object will retain its specific features and internal structure. In terms of implementation, the best way to represent a composite is to write a list of elements. The element class will be defined as pure virtual, which means we cannot create objects of this class directly, but need to derive other classes through inheritance. These derived classes will inherit all the attributes and methods of the pure virtual class but will also use extra attributes to encode class-specific information.
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A superlinearly convergent SSDP algorithm for nonlinear semidefinite programming

A superlinearly convergent SSDP algorithm for nonlinear semidefinite programming

Nonlinear semidefinite programming has many applications both in theory and in the real world. Many convex optimization problems, such as variational inequality problems, fixed point problems [1–3], can be reformulated as convex NLSDP. Robust control prob- lems, optimal structural design, and truss design problems can be reformulated as NLSDP (see [4–6]). There are a lot of literature for NLSDP on algorithms, for example, the aug- mented Lagrangian method [7–12], primal-dual interior point method [13, 14], and se- quential semidefinite programming (SSDP) method [15–21]. Our research focus is on the SSDP method.
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Performance Evaluation of Various Hyperspectral Nonlinear Unmixing Algorithms

Performance Evaluation of Various Hyperspectral Nonlinear Unmixing Algorithms

Recently, nonlinear unmixing for hyperspectral images is receiving attention in remote sensing image exploitation [6] [20] [4] and [18]. Alternative approximation approaches have been proposed for handling the effects of nonlinearity leading to utilizing physics-based nonlinear mixing models [1]. The bilinear mixture model (BMM), has been studied in several researches which is used with second-order scattering of photons between two different materials [8].

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Noise Dismissal from Images Using Nonlinear Algorithms

Noise Dismissal from Images Using Nonlinear Algorithms

International Journal of Scientific Research in Computer Science, Engineering and Information Technology CSEIT1833264 | Received 12 March 2018 | Accepted 24 March 2018 | March April 2018 [ (3 ) 3 839[.]

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SBB: A New Solver for Mixed Integer Nonlinear Programming

SBB: A New Solver for Mixed Integer Nonlinear Programming

Report and Stop Report and Stop Yes Unbounded Infeasible Integer Yes Store Solution Update Best Remove Nodes With worse Obj Yes.!. Local/Global Optimization Issues![r]

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Primal-Dual Path-Following Methods For Nonlinear Programming

Primal-Dual Path-Following Methods For Nonlinear Programming

Like many other primal-dual methods proposed by Gill and coauthors (see [10, 12, 18, 21]), the proposed algorithms treat both the primal and dual variables as independent variables at each iteration. This strategy is different from that of other methods that regard the dual variables as being dependent on the primal variables and update them each time the primal variables are modified. This can provide certain benefits, such as the ability to control the quality of the dual variables during the solution of each subproblem, and the ability to impose explicit bounds on the dual variables. These strategies can improve both the efficiency and the reliability of a method.
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INF 3 Algorithms and programming languages Modified

INF 3 Algorithms and programming languages Modified

A third-generation programming language (3GL) is a refinement of a second-generation programming language. Whereas a second generation language is more aimed to fix logical structure to the language, a third generation language aims to refine the usability of the language in such a way to make it more user friendly. This could mean restructuring categories of possible functions to make it more efficient, condensing the overall bulk of code via classes (eg. Visual Basic). A third generation language improves over a second generation language by having more refinement on the usability of the language itself from the perspective of the user.
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Duality in Nonlinear Fractional Programming Problem Using Fuzzy Programming and Genetic Algorithm

Duality in Nonlinear Fractional Programming Problem Using Fuzzy Programming and Genetic Algorithm

The duality theory for nonlinear multiobjective optimization problems in the field of the optimization theory has intensively developed during the last decades. In (Rodder and Zimmermann, 1980), a generalization of maxmin and minmax problems in a fuzzy environment is presented and thereby a pair of fuzzy dual linear programming problems is constructed. An economic interpretation of this duality in terms of market and industry is also discussed in that paper. In (Bector and Chandra, 2002), a pair of linear programming primal-dual problem is introduced under fuzzy environment and appropriate results were proved to establish the duality relationship between them. In (Liu, 1995) a constructive approach has been proposed to duality for fuzzy multiple criteria and multiple constraint level linear programming problems. (Biswas and Bose, 2012) gives a parametric approach for the duality in fuzzy multi criteria and multi constraint level linear programming problem. In (Gupta and Mehlawat , 2009), a study of a pair of fuzzy primal–dual linear programming problems has been presented and calculated duality results using an aspiration level approach using exponential membership function, while a discussion of fuzzy primal dual linear programming problem with fuzzy coefficients has been presented in (Bector and Chandra, 2002; Liu, 1995). In (Bector and Chandra, 2002), a pair of linear programming primal-dual problem is introduced under fuzzy environment and appropriate results were proved to establish the duality relationship between them. Also in (Chakraborty et al. 2014), author has presented a pair of linear primal – dual programming using linear and exponential membership function using fuzzy programming approach and genetic algorithm approach.
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