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In systems with hard real-time constraints, it is necessary to compute upper bounds on the worst-case execution time (WCET) of programs; the closer the bound to the real WCET, the better. This is especially the case of synchronous reactive control loops with a fixed clock; the WCET of the loop body must not exceed the clock period. We compute the WCET (or at least a close upper bound thereof) as the solution of an optimization modulo theory problem that takes into account the semantics of the program, in contrast to other methods that compute the longest path whether or not it is feasible according to these semantics. Optimization modulo theory extends satisfiability modulo theory (SMT) to maximization problems. Immediate encodings of WCET problems into SMT yield formulas intractable for all current production-grade solvers; this is inherent to the DPLL(T) approach to SMT implemented in these solvers. By conjoining some appropriate "cuts" to these formulas, we considerably reduce the computation time of the SMT-solver. We experimented our approach on a variety of control programs, using the OTAWA analyzer both as baseline and as underlying microarchitectural analysis for our analysis, and show notable improvement on the WCET bound on a variety of benchmarks and control programs.

In systems with hard real-time constraints, it is necessary to compute upper bounds on the worst-case execution time (WCET) of programs; the closer the bound to the real WCET, the better. This is especially the case of synchronous reactive control loops with a fixed clock; the WCET of the loop body must not exceed the clock period. We compute the WCET (or at least a close upper bound thereof) as the solution of an optimization modulo theory problem that takes into account the semantics of the program, in contrast to other methods that compute the longest path whether or not it is feasible according to these semantics. Optimization modulo theory extends satisfiability modulo theory (SMT) to maximization problems. Immediate encodings of WCET problems into SMT yield formulas intractable for all current production-grade solvers; this is inherent to the DPLL(T) approach to SMT implemented in these solvers. By conjoining some appropriate "cuts" to these formulas, we considerably reduce the computation time of the SMT-solver. We experimented our approach on a variety of control programs, using the OTAWA analyzer both as baseline and as underlying microarchitectural analysis for our analysis, and show notable improvement on the WCET bound on a variety of benchmarks and control programs.

As a prerequisite to dynamical fermion simulations a detailed study of optimal parameters and scaling behavior is conducted for the quenched Schr\"odinger functional at fixed renormalized coupling. We compare standard hybrid overrelaxation techniques with local and global hybrid Monte Carlo. Our efficiency measure is designed to be directly relevant for the strong coupling constant as used by the ALPHA collaboration.

Journal of Optimization Theory and Applications 2002: Volume 112 , Issue Contents. Digitized from IA1534532-02 . Previous issue: sim_journal-of-optimization-theory-and-applications_2001-12_111_3 . Next issue: sim_journal-of-optimization-theory-and-applications_2002-01_112_1 .

Graduate research activity in the following areas is reported: the divergence of laminated composite lifting surfaces, subsonic propeller theory and aeroelastic analysis, and cross sectional resonances in wind tunnels.

Journal of Optimization Theory and Applications 1979: Volume 29 , Issue Index. Digitized from IA1534532-04 . Previous issue: sim_journal-of-optimization-theory-and-applications_1979-08_28_4 . Next issue: sim_journal-of-optimization-theory-and-applications_1979_29_contents .

Journal of Optimization Theory and Applications 1979: Volume 29 , Issue Index. Digitized from IA1534532-04 . Previous issue: sim_journal-of-optimization-theory-and-applications_1979-08_28_4 . Next issue: sim_journal-of-optimization-theory-and-applications_1979_29_contents .

An extension of classical methods of optimal control theory for systems described by ordinary differential equations to distributed-parameter systems described by partial differential equations is presented. An application is given involving the minimum-mass design of a simply-supported shear plate with a fixed fundamental frequency of vibration. An optimal plate thickness distribution in analytical form is found. The case of a minimum-mass design of an elastic sandwich plate whose fundamental frequency of free vibration is fixed. Under the most general conditions, the optimization problem reduces to the solution of two simultaneous partial differential equations involving the optimal thickness distribution and the modal displacement. One equation is the uniform energy distribution expression which was found by Ashley and McIntosh for the optimal design of one-dimensional structures with frequency constraints, and by Prager and Taylor for various design criteria in one and two dimensions. The second equation requires dynamic equilibrium at the preassigned vibration frequency.

A linearized theory wing design and optimization procedure which allows physical realism and practical considerations to be imposed as constraints on the optimum (least drag due to lift) solution is discussed and examples of application are presented. In addition to the usual constraints on lift and pitching moment, constraints are imposed on wing surface ordinates and wing upper surface pressure levels and gradients. The design procedure also provides the capability of including directly in the optimization process the effects of other aircraft components such as a fuselage, canards, and nacelles.

A working theory of two-component two-phase (gas-liquid) nozzles, developed as part of Phase I, was used to conduct parametric performance analyses over a wide range of flow conditions and for varied fluid properties. Droplet breakup and the interface transport of momentum and thermal energy were considered in the analyses, but mass transfer and wall friction losses were excluded. Results from the parametric analyses compare favorably with experimental data and other theoretical analyses available in the literature. The sensitivity of the predicted nozzle performance to the assumptions concerning the droplet characteristics was examined, and criteria were developed for optimum nozzle designs based on the parametric analyses. A simplified procedure was developed for the quick estimation of the major dimensions for two-phase nozzles. Finally, the work extraction potential of two-phase nozzles was examined in terms of their performance criteria and system integration characteristics.

In the Internet today, traffic management spans congestion control (at end hosts), routing protocols (on routers), and traffic engineering (by network operators). Historically, this division of functionality evolved organically. This talk presents a top-down redesign of traffic management using recent innovations in optimization theory. First, we propose an objective function that captures the goals of end users and network operators. Using all known optimization decomposition techniques, we generate four distributed algorithms that divide traffic over multiple paths based on feedback from the network links. Combining the best features of the algorithms, we construct a traffic management protocol that is distributed, adaptive, robust, flexible and easy to manage. Further, our new protocol can operate based on implicit feedback about packet loss and delay. We show that using optimization decompositions as a foundation, simulations as a building block, and human intuition as a guide can be a principled approach to protocol design. This is joint work with Jiayue He, Martin Suchara, Ma’ayan Bresler, and Mung Chiang. ©2009 Microsoft Corporation. All rights reserved.

The research described in this report deals with the development of (1) efficient, consistent discretization techniques for use in semi-infinite optimization, optimal control algorithms that solve problems with dynamics governed by partial differential equations, (2) superlinearly converging semi- finite optimization algorithms, (3) various techniques for finding design parameters satisfying specifications, (4) optimal control algorithms for discrete and distributed systems with control, state and shape constraints, and (5) efficient numerical procedures for the integrated design of flexible structures and their control systems.... Optimization, Optimal shape design algorithms.

In the Cellular Dynamical Mean Field Theory (CDMFT), a strongly correlated system is represented by a small cluster of correlated sites, coupled to an adjustable bath of uncorrelated sites simulating the cluster's environment; the parameters governing the bath are set by a self-consistency condition involving the local Green function and the lattice electron dispersion. Solving the cluster problem with an exact diagonalization method is only practical for small bath sizes (8 sites). In that case the self-consistency condition cannot be exactly satisfied and is replaced by a minimization procedure. There is some freedom in the definition of the `merit function' to optimize. We use Potthoff's Self-Energy Functional Approach on the one- and two-dimensional Hubbard models to gain insight into the best choice for this merit function. We argue that several merit functions should be used and preference given to the one that leads to the smallest gradient of the Potthoff self-energy functional. We propose a new merit function weighted with the self-energy that seems to fit the Mott transition in two dimensions better than other merit functions.

This paper considers optimization over multiple renewal systems coupled by time average constraints. These systems act asynchronously over variable length frames. For each system, at the beginning of each renewal frame, it chooses an action which affects the duration of its own frame, the penalty, and the resource expenditure throughout the frame. The goal is to minimize the overall time average penalty subject to several overall time average resource constraints which couple these systems. This problem has applications to task processing networks, coupled Markov decision processes(MDPs) and so on. We propose a distributed algorithm so that each system can make its own decision after observing a global multiplier which is updated slot-wise. We show that this algorithm satisfies the desired constraints and achieves $\mathcal{O}(\varepsilon)$ near optimality with $\mathcal{O}(1/\varepsilon^2)$ convergence time.

In the Internet today, traffic management spans congestion control (at end hosts), routing protocols (on routers), and traffic engineering (by network operators). Historically, this division of functionality evolved organically. This talk presents a top-down redesign of traffic management using recent innovations in optimization theory. First, we propose an objective function that captures the goals of end users and network operators. Using all known optimization decomposition techniques, we generate four distributed algorithms that divide traffic over multiple paths based on feedback from the network links. Combining the best features of the algorithms, we construct a traffic management protocol that is distributed, adaptive, robust, flexible and easy to manage. Further, our new protocol can operate based on implicit feedback about packet loss and delay. We show that using optimization decompositions as a foundation, simulations as a building block, and human intuition as a guide can be a principled approach to protocol design. This is joint work with Jiayue He, Martin Suchara, Ma’ayan Bresler, and Mung Chiang. ©2009 Microsoft Corporation. All rights reserved.

In this paper we develop a general convergence theory for a class of quasi-Newton methods for equality constrained optimization. The theory is set in the framework of the diagonalized multiplier method defined by Tapia and is an extension of the theory developed by Glad. We believe that this framework is flexible and amenable to convergence analysis and generalizations. A key ingredient of a method in this class is a multiplier update. Our theory is tested by showing that a straightforward application gives the best known convergence results for several known multiplier updates. Also a characterization of q.superlinear convergence is presented. It is shown that in the special case when the diagonalized multiplier method is equivalent to the successive quadratic programming approach, our general characterization result gives the Boggs, Tolle and Wang characterization.

In this paper we develop a general convergence theory for a class of quasi-Newton methods for equality constrained optimization. The theory is set in the framework of the diagonalized multiplier method defined by Tapia and is an extension of the theory developed by Glad. We believe that this framework is flexible and amenable to convergence analysis and generalizations. A key ingredient of a method in this class is a multiplier update. Our theory is tested by showing that a straightforward application gives the best known convergence results for several known multiplier updates. Also a characterization of q.superlinear convergence is presented. It is shown that in the special case when the diagonalized multiplier method is equivalent to the successive quadratic programming approach, our general characterization result gives the Boggs, Tolle and Wang characterization.

Trajectory physics optimization for boundary value problem solution

This series of lectures on optimization techniques was originally prepared for presentation to the participants of the 1959 Associate Staff Training Program. Its objectives were two-fold: (a) to introduce engineers to the usefulness of mathematical techniques and mathematicians to the applicability of their techniques to concrete engineering problems and (b) to introduce all the trainees to the very important concept of optimization--a concept that currently pervades virtually every area of advanced missile engineering.

The relationship between point-mass and spherical harmonic expansions of anomalous gravity is analyzed. In particular, the spherical harmonic expansion of a point-mass set is determined and used to discuss the nature of point-mass solutions to the problem of modeling anomalous gravity. Also, a 'global error', which arises when a finite spherical harmonic expansion is replaced by a point-mass set, is first defined and then determined, both for the anomalous potential and its gradient. Defining an optimal point-mass set as one which minimizes the gradient error leads to a system of equations (linear in the masses, but non-linear in their positions) whose solution is the optimal point-mass set. Methods of solution are also discussed. (Author)

Recent empirical research indicates that many convex optimization problems with random constraints exhibit a phase transition as the number of constraints increases. For example, this phenomenon emerges in the l1 minimization method for identifying a sparse vector from random linear samples. Indeed, this approach succeeds with high probability when the number of samples exceeds a threshold that depends on the sparsity level; otherwise it fails with high probability. This paper provides the first rigorous analysis that explains why phase transitions are ubiquitous in random convex optimization problems. It also describes tools for making reliable predictions about the quantitative aspects of the transition, including the location and the width of the transition region. These techniques apply to regularized linear inverse problems with random measurements, to demixing problems under a random incoherence model and also to cone programs with random affine constraints. These applications depend on foundational research in conic geometry. This paper introduces a new summary parameter, called the statistical dimension, that canonically extends the dimension of a linear subspace to the class of convex cones. The main technical result demonstrates that the sequence of conic intrinsic volumes of a convex cone concentrates sharply near the statistical dimension. This fact leads to an approximate version of the conic kinematic formula that gives bounds on the probability that a randomly oriented cone shares a ray with a fixed cone.

This paper presents a detailed proof of the triality theorem for a class of fourth-order polynomial optimization problems. The method is based on linear algebra but it solves an open problem on the double-min duality left in 2003. Results show that the triality theory holds strongly in a tri-duality form if the primal problem and its canonical dual have the same dimension; otherwise, both the canonical min-max duality and the double-max duality still hold strongly, but the double-min duality holds weakly in a symmetrical form. Four numerical examples are presented to illustrate that this theory can be used to identify not only the global minimum, but also the largest local minimum and local maximum.

In analyzing information streamed by sensory organs, our brains face challenges similar to those solved in statistical signal processing. This suggests that biologically plausible implementations of online signal processing algorithms may model neural computation. Here, we focus on such workhorses of signal processing as Principal Component Analysis (PCA) and whitening which maximize information transmission in the presence of noise. We adopt the similarity matching framework, recently developed for principal subspace extraction, but modify the existing objective functions by adding a decorrelating term. From the modified objective functions, we derive online PCA and whitening algorithms which are implementable by neural networks with local learning rules, i.e. synaptic weight updates that depend on the activity of only pre- and postsynaptic neurons. Our theory offers a principled model of neural computations and makes testable predictions such as the dropout of underutilized neurons.

Convex optimization problems arise naturally in quantum information theory, often in terms of minimizing a convex function over a convex subset of the space of hermitian matrices. In most cases, finding exact solutions to these problems is usually impossible. As inspired by earlier investigations into the relative entropy of entanglement [Phys. Rev. A 78 032310 (2008)], we introduce a general method to solve the converse problem rather than find explicit solutions. That is, given a matrix in a convex set, we determine a family of convex functions that are minimized at this point. This method allows us find explicit formulae for the relative entropy of entanglement and the Rains bound, two well-known upper bounds on the distillable entanglement, and yields interesting information about these quantities, such as the fact that they coincide in the case where at least one subsystem of a multipartite state is a qubit.

We propose and study a simple model of dynamical redistribution of capital in a diversified portfolio. We consider a hypothetical situation of a portfolio composed of N uncorrelated stocks. Each stock price follows a multiplicative random walk with identical drift and dispersion. The rules of our model naturally give rise to power law tails in the distribution of capital fractions invested in different stocks. The exponent of this scale free distribution is calculated in both discrete and continuous time formalism. It is demonstrated that the dynamical redistribution strategy results in a larger typical growth rate of the capital than a static ``buy-and-hold'' strategy. In the large N limit the typical growth rate is shown to asymptotically approach that of the expectation value of the stock price. The finite dimensional variant of the model is shown to describe the partition function of directed polymers in random media.

Journal of Optimization Theory and Applications 1985: Volume 47 , Issue Contents. Digitized from IA1534532-04 . Previous issue: sim_journal-of-optimization-theory-and-applications_1985-08_46_4 . Next issue: sim_journal-of-optimization-theory-and-applications_1985-09_47_1 .

Journal of Optimization Theory and Applications 1989: Volume 63 , Issue Contents. Digitized from IA1534532-03 . Previous issue: sim_journal-of-optimization-theory-and-applications_1989-09_62_3 . Next issue: sim_journal-of-optimization-theory-and-applications_1989-10_63_1 .

Journal of Optimization Theory and Applications 1991: Volume 71 , Issue Contents. Digitized from IA1534532-03 . Previous issue: sim_journal-of-optimization-theory-and-applications_1991-09_70_3 . Next issue: sim_journal-of-optimization-theory-and-applications_1991-10_71_1 .

Journal of Optimization Theory and Applications 1998: Volume 97 , Issue Contents. Digitized from IA1534532-03 . Previous issue: sim_journal-of-optimization-theory-and-applications_1998-03_96_3 . Next issue: sim_journal-of-optimization-theory-and-applications_1998-04_97_1 .

Journal of Optimization Theory and Applications 1999: Volume 102 , Issue Contents. Digitized from IA1534532-03 . Previous issue: sim_journal-of-optimization-theory-and-applications_1999-06_101_3 . Next issue: sim_journal-of-optimization-theory-and-applications_1999-07_102_1 .

In this project of Information-theory and Optimization of Networked Systems (IONS), we developed the approaches of geometric programming, information-control duality, erasure information, and stochastic network optimization for communication networks. Pathfinder applications to missile defense, telecommunications, and biological networks are studied. Several major awards were received by the team of co-PIs, including Shannon Award, IEEE Hamming Medal, Presidential Early Career Award for Scientists and Engineers, and Technology Review TR35 Young Innovator Award.

The distribution generation penetration increases due to the increased need of power. These penetration causes problem of frequency deviations. In this paper the in multi-area power system the combination of renewable energy resources is proposed. Here the area 1 & area 2 are thermal power plant. Hydro power generation plant is taken in area-3 & area-4. Renewable energy system is considered in area-5. Here the cyber security attack is taken as change in power in the entire area. This can make the power block out or wrong data entry. Here differential game theory-based problem formulation is done. The PI controller and differential game theory with flower pollination are compared for performance of fast response. The MATLAB 2017b is used for building the areas and programming the algorithm.

The talk focuses on expander graphs in conjunction with the combined use of SDPs and eigenvalue techniques for approximating optimal solutions to combinatorial optimization problems. In the first part of the talk I will explain how to construct cost-effective, expanding networks by using 'local' sparsifiers of graphs that emerge as a solution to a semidefinite program. In the second part of the talk I will show that the Unique Games Conjecture is false when the underlying constraint graph is a (spectral) expander. Namely, I will present a polynomial-time algorithm for Unique Games on expanding instances that finds a good assignment when there exists one. ©2009 Microsoft Corporation. All rights reserved.

The talk focuses on expander graphs in conjunction with the combined use of SDPs and eigenvalue techniques for approximating optimal solutions to combinatorial optimization problems. In the first part of the talk I will explain how to construct cost-effective, expanding networks by using 'local' sparsifiers of graphs that emerge as a solution to a semidefinite program. In the second part of the talk I will show that the Unique Games Conjecture is false when the underlying constraint graph is a (spectral) expander. Namely, I will present a polynomial-time algorithm for Unique Games on expanding instances that finds a good assignment when there exists one. ©2009 Microsoft Corporation. All rights reserved.

The talk focuses on expander graphs in conjunction with the combined use of SDPs and eigenvalue techniques for approximating optimal solutions to combinatorial optimization problems. In the first part of the talk I will explain how to construct cost-effective, expanding networks by using 'local' sparsifiers of graphs that emerge as a solution to a semidefinite program. In the second part of the talk I will show that the Unique Games Conjecture is false when the underlying constraint graph is a (spectral) expander. Namely, I will present a polynomial-time algorithm for Unique Games on expanding instances that finds a good assignment when there exists one. ©2009 Microsoft Corporation. All rights reserved.

Journal of Optimization Theory and Applications 2002: Volume 113 , Issue Contents. Digitized from IA1534532-02 . Previous issue: sim_journal-of-optimization-theory-and-applications_2002-03_112_3 . Next issue: sim_journal-of-optimization-theory-and-applications_2002-04_113_1 .

Journal of Optimization Theory and Applications 2002: Volume 113 , Issue Contents. Digitized from IA1534532-02 . Previous issue: sim_journal-of-optimization-theory-and-applications_2002-03_112_3 . Next issue: sim_journal-of-optimization-theory-and-applications_2002-04_113_1 .

A working theory of two-component two-phase (gas-liquid) nozzles, developed as part of Phase I, was used to conduct parametric performance analyses over a wide range of flow conditions and for varied fluid properties. Droplet breakup and the interface transport of momentum and thermal energy were considered in the analyses, but mass transfer and wall friction losses were excluded. Results from the parametric analyses compare favorably with experimental data and other theoretical analyses available in the literature. The sensitivity of the predicted nozzle performance to the assumptions concerning the droplet characteristics was examined, and criteria were developed for optimum nozzle designs based on the parametric analyses. A simplified procedure was developed for the quick estimation of the major dimensions for two-phase nozzles. Finally, the work extraction potential of two-phase nozzles was examined in terms of their performance criteria and system integration characteristics.

A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy 作者： Baiyi Wang 1 Zipeng Zhang 1 Patrick Siarry 2 Xinhua Liu 1 Grzegorz Królczyk 3 Dezheng Hua 1 Frantisek Brumercik 4 Zhixiong Li 3 作者单位： 1. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, China 2. University Paris-Est Créteil Val de Marne, 61 Av. du General de Gaulle, Creteil 94010, France 3. Department of Manufacturing Engineering and Automation Products, Opole University of Technology, Opole 45-758, Poland 4. Department of Design and Machine Elements, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 1, Zilina 010 26, Slovakia 通讯作者： Xinhua Liu Email:[email protected] 提交时间： 2024-03-28 18:49:16 摘要: As a new intelligent optimization algorithm, the African vultures optimization algorithm (AVOA) has been widely used in various fields today. However, when solving complex multimodal problems, the AVOA still has some shortcomings, such as low searching accuracy, deficiency on the search capability and tendency to fall into local optimum. In order to alleviate the main shortcomings of the AVOA, a nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture’s initial population; Furthermore, the nonlinear adaptive incremental inertial weight factor is introduced in the location update phase to rationally balance the exploration and exploitation abilities, and avoid individual falling into a local optimum; The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution and strengthen the ability to jump out of the local optimal solution. HWEAVOA and other advanced comparison algorithms are used to solve classical and CEC2022 test functions. Compared with other algorithms, the convergence curves of the HWEAVOA drop faster and the line bodies are smoother. These experimental results show the proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability. Meanwhile, HWEAVOA has reached the general level in the algorithm complexity, and its overall performance is competitive in the swarm intelligence algorithms. African vultures optimization algorithm Henon chaotic mapping theory Nonlinear adaptive incremental inertial weight factor Reverse learning competition strategy 来自： Zhixiong Li 分类： 工程与技术科学 >> 工程通用技术 投稿状态： 已被期刊接收 引用： ChinaXiv:202403.00412 (或此版本 ChinaXiv:202403.00412V1 ) DOI:10.12074/202403.00412V1 CSTR:32003.36.ChinaXiv.202403.00412.V1 推荐引用方式： Baiyi Wang ,Zipeng Zhang,Patrick Siarry,Xinhua Liu,Grzegorz Królczyk,Dezheng Hua,Frantisek Brumercik,Zhixiong Li.(2024).A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy.中国科学院科技论文预发布平台.[ChinaXiv:202403.00412] 版本历史 [V1] 2024-03-28 18:49:16 ChinaXiv:202403.00412V1 下载全文

This paper describes the implementation of optimization techniques based on control theory for airfoil design. In our previous work in the area it was shown that control theory could be employed to devise effective optimization procedures for two-dimensional profiles by using either the potential flow or the Euler equations with either a conformal mapping or a general coordinate system. We have also explored three-dimensional extensions of these formulations recently. The goal of our present work is to demonstrate the versatility of the control theory approach by designing airfoils using both Hicks-Henne functions and B-spline control points as design variables. The research also demonstrates that the parameterization of the design space is an open question in aerodynamic design.

Journal of Optimization Theory and Applications 1995: Volume 84 , Issue Contents. Digitized from IA1534532-03 . Previous issue: sim_journal-of-optimization-theory-and-applications_1994-12_83_3 . Next issue: sim_journal-of-optimization-theory-and-applications_1995-01_84_1 .

Journal of Optimization Theory and Applications 2001: Volume 109 , Issue Contents. Digitized from IA1534532-02 . Previous issue: sim_journal-of-optimization-theory-and-applications_2001-03_108_3 . Next issue: sim_journal-of-optimization-theory-and-applications_2001-04_109_1 .

Journal of Optimization Theory and Applications 1977: Volume 21 , Issue Index. Digitized from IA1534532-04 . Previous issue: sim_journal-of-optimization-theory-and-applications_1976-12_20_4 . Next issue: sim_journal-of-optimization-theory-and-applications_1977-01_21_1 .

Journal of Optimization Theory and Applications 1985: Volume 45 , Issue Contents. Digitized from IA1534532-04 . Previous issue: sim_journal-of-optimization-theory-and-applications_1984-12_44_4 . Next issue: sim_journal-of-optimization-theory-and-applications_1985-01_45_1 .

Journal of Optimization Theory and Applications 1987: Volume 52 , Issue Contents. Digitized from IA1534532-03 . Next issue: sim_journal-of-optimization-theory-and-applications_1987-01_52_1 .

Journal of Optimization Theory and Applications 1992: Volume 72 , Issue Contents. Digitized from IA1534532-03 . Previous issue: sim_journal-of-optimization-theory-and-applications_1991-12_71_3 . Next issue: sim_journal-of-optimization-theory-and-applications_1992-01_72_1 .

Journal of Optimization Theory and Applications 1992: Volume 72 , Issue Contents. Digitized from IA1534532-03 . Previous issue: sim_journal-of-optimization-theory-and-applications_1991-12_71_3 . Next issue: sim_journal-of-optimization-theory-and-applications_1992-01_72_1 .

A theory of financial instability, positively verified through adverse consequences of the global financial crisis that started in 2007, demonstrates the significance put on adequate decision making in present age. Thus, optimization of operations in this respect applies to providing suitable frameworks for supervision and functioning of financial markets which may be more predictable and secure for individual stakeholders.