Saddle Point Condition Research Articles

New Classes of Solutions for Euclidean Scalar Field Theories

This paper presents new classes of exact radial solutions to the nonlinear ordinary differential equation that arises as a saddle-point condition for a Euclidean scalar field theory in D-dimensional spacetime. These solutions are found by exploiting the dimensional consistency of the radial differential equation for a single massless scalar field, which allows it to transform into an autonomous equation. For massive theories, the radial equation is not exactly solvable, but the massless solutions provide useful approximations to the results for the massive case. The solutions presented here depend on the power of the interaction and on the spatial dimension, both of which may be noninteger. Scalar equations arising in the study of conformal invariance fit into this framework, and classes of new solutions are found. These solutions exhibit two distinct behaviors as D→2 from above.

Sufficient optimality, duality and saddle point analysis in terms of convexificators for nonsmooth robust fractional interval-valued optimization problems

In this study, sufficient optimality criteria for a robust fractional interval-valued optimization problem (RP) is highlighted based on the idea of convexificators. The use of generalized invexity tools to support sufficient optimality conditions is demonstrated by an example. Also, saddle point condition of a Lagrange function is examined for RP. Furthermore, Mond–Weir-type robust dual model is constructed and by employing the concept of generalized invexity, usual duality results are discussed.

Convexificators for nonconvex multiobjective optimization problems with uncertain data: robust optimality and duality

In this paper, we investigate robust optimality conditions and duality for a class of nonconvex multiobjective optimization problems with uncertain data in the worst case by the upper semi-regular convexificator. The Fermat principle for a locally Lipschitz function is presented in terms of the upper semi-regular convexificator. We establish robust necessary optimality conditions of the Fritz-John type and KKT type for the uncertain nonconvex multiobjective optimization problems. In addition, robust sufficient optimality conditions as well as saddle point conditions are derived under the generalized ∂ ˆ ∗ -pseudoquasiconvexity and generalized convexity, respectively. The robust duality relations between the original problem and its mixed robust dual problem are obtained under a generalized pseudoconvexity assumption.

Robust Radar Waveform Optimization Under Target Interpulse Fluctuation and Practical Constraints via Sequential Lagrange Dual Approximation

This correspondence considers the problem of robust radar waveform design against target interpulse fluctuation with constraints on the transmit energy, similarity, and signal dynamic range. We aim to make robust the transmit waveform and receive filter with respect to the Target Amplitude Vector (TAV) uncertainties by enhancing the worst-case Signal-to-Noise-Ratio (SNR) over the modeled ellipsoidal set. First of all, the filter is directly solved leveraging on the existence of saddle point condition, which leads to a suitable simplification of the original non-convex problem. Then, to handle the resultant max-min problem, the Sequential Lagrange Dual Approximation (SLDA) procedure is devised. Specifically, we update the transmit waveform via solving a sequence of max-min approximate problems, which are proven to be hidden convex relying on the theory of Lagrange duality. Finally, the filter is synthesized after solving a Quadratical Constraint Quadratic Programming (QCQP) problem based on the optimized waveform. Numerical results highlight the robustness of the proposed design which guarantees an enhanced worst-case SNR with respect to TAV uncertainties.

Effects of nuclear side reactions on fusion triple product and saddle point condition in the spherical torus

Effects of nuclear side reactions on fusion triple product and saddle point condition in the spherical torus

2D Electron System with Quasiparticle Auxiliary Field Correlation of Open quantum System

The objective of this work is to develop the mathematical model for the auxiliary field and interlink the pair correlation in the external potential of the open quantum system of the 2D electron system. For this, we used 2D Hubbard Model with the action open quantum system Lindblad equation and then apply the saddle point condition to obtain the auxiliary field of the 2D system. The obtained pair correlation of quasiparticles was found symmetric in nature with cooper pairs electron paring angle. The pair correlation of 0K was found 0.1times, 1 time, 2 times, 3 times, 4 times, and 5 times greater when compared with 0.2K, 0.3K, 0.4K, 0.5K, and 0.6K, respectively at -1 radian and +1 radian copper pairs electron pairing angle which is maximum. In addition, with increasing the temperature, the pair correlation decrease and become minimum without loss of symmetry.

Robust Discrete-Time Pontryagin Maximum Principle on Matrix Lie Groups

This article considers a discrete-time robust optimal control problem on matrix Lie groups. The underlying system is assumed to be perturbed by exogenous unmeasured bounded disturbances, and the control problem is posed as a min–max optimal control wherein the disturbance is the adversary and tries to maximize a cost that the control tries to minimize. Assuming the existence of a saddle point in the problem, we present a version of the Pontryagin maximum principle (PMP) that encapsulates first-order necessary conditions that the optimal control and disturbance trajectories must satisfy. This PMP features a saddle point condition on the Hamiltonian and a set of backward difference equations for the adjoint dynamics. We also present a special case of our result on Euclidean spaces. We conclude with applying the PMP to robust version of single axis rotation of a rigid body.

Approximations for Pareto and Proper Pareto solutions and their KKT conditions

In this article, we view the Pareto and weak Pareto solutions of the multiobjective optimization by using an approximate version of KKT type conditions. In one of our main results Ekeland’s variational principle for vector-valued maps plays a key role. We also focus on an improved version of Geoffrion proper Pareto solutions and it’s approximation and characterize them through saddle point and KKT type conditions.

Integral F(R) gravity and saddle point condition as a remedy for the H0-tension

In this work, we shall provide an F(R) gravity theoretical framework for solving the H0-tension. Specifically, by exploiting the F(R) gravity correspondence with a scalar-tensor theory, we shall provide a condition in which when it is satisfied, the H0-tension is alleviated. The condition that remedies the H0-tension restricts the corresponding F(R) gravity, and we present in brief the theoretical features of the constrained F(R) gravity theory in both the Jordan and Einstein frames. The condition that may remedy the H0-tension is based on the existence of a metastable de Sitter point that occurs for redshifts near the recombination. This metastable de Sitter vacuum restricts the functional form of the F(R) gravity in the Jordan frame. We also show that by appropriately choosing the F(R) gravity, along with the theoretical solution offered for the H0-tension problem, one may also provide a unified description of the inflationary era with the late-time accelerating era, in terms of two extra de Sitter vacua. We propose a new approach to F(R) gravity by introducing a new class of integral F(R) gravity functions, which may be wider than the usual class expressed in terms of elementary F(R) gravity functions. Finally, the Einstein frame inflationary dynamics formalism is briefly discussed.

Optimal Guidance Laws for a Hypersonic Multiplayer Pursuit-Evasion Game Based on a Differential Game Strategy

The guidance problem of a confrontation between an interceptor, a hypersonic vehicle, and an active defender is investigated in this paper. As a hypersonic multiplayer pursuit-evasion game, the optimal guidance scheme for each adversary in the engagement is proposed on the basis of linear-quadratic differential game strategy. In this setting, the angle of attack is designed as the output of guidance laws, in order to match up with the nonlinear dynamics of adversaries. Analytical expressions of the guidance laws are obtained by solving the Riccati differential equation derived by the closed-loop system. Furthermore, the satisfaction of the saddle-point condition of the proposed guidance laws is proven mathematically according to the minimax principle. Finally, nonlinear numerical examples based on 3-DOF dynamics of hypersonic vehicles are presented, to validate the analytical analysis in this study. By comparing different guidance schemes, the effectiveness of the proposed guidance strategies is demonstrated. Players in the engagement could improve their performance in confrontation by employing the proposed optimal guidance approaches with appropriate weight parameters.

Functional-integral approach to Gaussian fluctuations in Eliashberg theory

The Eliashberg theory of superconductivity is based on a dynamical electron-phonon interaction as opposed to a static interaction present in BCS theory. The standard derivation of Eliashberg theory is based on an equation of motion approach, which incorporates certain approximations such as Migdal's approximation for the pairing vertex. In this paper we provide a functional-integral-based derivation of Eliashberg theory and we also consider its Gaussian-fluctuation extension. The functional approach enables a self-consistent method of computing the mean-field equations, which arise as saddle-point conditions, and here we observe that the conventional Eliashberg self energy and pairing function both appear as Hubbard-Stratonovich transformations. An important consequence of this fact is that it provides a systematic derivation of the Cooper and density-channel interactions in the Gaussian fluctuation response. We also investigate the strong-coupling fluctuation diamagnetic susceptibility near the critical temperature.

Extremal Shift to Accompanying Points in a Positional Differential Game for a Fractional-Order System

A two-person zero-sum differential game is considered. The motion of the dynamic system is described by an ordinary differential equation with a Caputo fractional derivative of order α ∈ (0, 1). The quality index consists of two terms: the first depends on the motion of the system realized by the terminal time and the second includes an integral estimate of the realizations of the players’ controls. The positional approach is applied to formalize the game in the “strategy–counterstrategy” and “counterstrategy–strategy” classes as well as in the “strategy–strategy” classes under the additional saddle point condition in the small game. In each case, the existence of the value and of the saddle point of the game is proved. The proofs are based on an appropriate modification of the method of extremal shift to accompanying points, which takes into account the specific properties of fractional-order systems.

Optimal guidance against active defense ballistic missiles via differential game strategies

The optimal guidance problem for an interceptor against a ballistic missile with active defense is investigated in this paper. A class of optimal guidance schemes are proposed based on linear quadratic differential game method and numerical solution of Riccati differential equation. By choosing proper parameters, the proposed guidance schemes are able to drive the interceptor to the target and away from the defender simultaneously. Additionally, fuel cost, control saturation, chattering phenomenon and parameters selection were taken into account. Satisfaction of the proposed guidance schemes of the saddle point condition is proven theoretically. Finally, nonlinear numerical examples are included to demonstrate the effectiveness and performance of the developed guidance approaches. Comparison of control performance between different guidance schemes are presented and analysis.

Nonlinear separation methods and applications for vector equilibrium problems using improvement sets

ABSTRACT In this paper, the image space analysis is applied to investigate a vector equilibrium problem using improvement sets and with matrix inequality constraints. First, a nonlinear scalar regular weak separation function is constructed by using the oriented distance function and the norm function. Then, a global saddle-point condition for a generalized Lagrange function is investigated. It is shown that the existence of a saddle point is equivalent to a nonlinear separation of two suitable subsets in the image space. Furthermore, a gap function and an error bound are obtained in terms of the nonlinear scalar regular weak separation function under suitable assumptions. As applications, an optimality condition, a gap function and an error bound for a strategic game with vector payoffs are also given.

Scalarizations and Optimality of Constrained Set-Valued Optimization Using Improvement Sets and Image Space Analysis

In this paper, we aim at applying improvement sets and image space analysis to investigate scalarizations and optimality conditions of the constrained set-valued optimization problem. Firstly, we use the improvement set to introduce a new class of generalized convex set-valued maps. Secondly, under suitable assumptions, some scalarization results of the constrained set-valued optimization problem are obtained in the sense of (weak) optimal solution characterized by the improvement set. Finally, by considering two classes of nonlinear separation functions, we present the separation between two suitable sets in image space and derive some optimality conditions for the constrained set-valued optimization problem. It shows that the existence of a nonlinear separation is equivalent to a saddle point condition of the generalized Lagrangian set-valued functions.

Robust Policies for a Multiple-Pursuer Single-Evader Differential Game

Analysis of the pursuit–evasion differential game consisting of multiple pursuers and single evader with simple motion is difficult due to the well-known curse of dimensionality. Policies have been proposed for this scenario, and we show that these policies are global Stackelberg equilibrium strategies. However, we also show that they are not saddle-point equilibria in the feedback sense. The argument is twofold: cases where the saddle-point condition is violated and cases where the strategy profiles are not time consistent (subgame perfect). The issue of capturability is explored, and sufficient conditions for guaranteed capture are provided. A new pursuit policy is proposed which guarantees capture while also providing an upper bound for capture time. The evader policy corresponding to the global Stackelberg equilibrium is shown to provide a lower bound for capture time. Thus, these policies are robust from the pursuer and evader perspectives, respectively, should they implement them. Several other interesting pursuit and evasion policies are explored and compared with the robust policies in a series of experiments.

Proper or Weak Efficiency via Saddle Point Conditions in Cone-Constrained Nonconvex Vector Optimization Problems

Motivated by many applications (for instance, some production models in finance require infinity-dimensional commodity spaces, and the preference is defined in terms of an ordering cone having possibly empty interior), this paper deals with a unified model, which involves preference relations that are not necessarily transitive or reflexive. Our study is carried out by means of saddle point conditions for the generalized Lagrangian associated with a cone-constrained nonconvex vector optimization problem. We establish a necessary and sufficient condition for the existence of a saddle point in case the multiplier vector related to the objective function belongs to the quasi-interior of the polar of the ordering set. Moreover, exploiting suitable Slater-type constraints qualifications involving the notion of quasi-relative interior, we obtain several results concerning the existence of a saddle point, which serve to get efficiency, weak efficiency and proper efficiency. Such results generalize, to the nonconvex vector case, existing conditions in the literature. As a by-product, we propose a notion of properly efficient solution for a vector optimization problem with explicit constraints. Applications to optimality conditions for vector optimization problems are provided with particular attention to bicriteria problems, where optimality conditions for efficiency, proper efficiency and weak efficiency are stated, both in a geometric form and by means of the level sets of the objective functions.

Strong Lagrange duality and the maximum principle for nonlinear discrete time optimal control problems

We examine discrete-time optimal control problems with general, possibly non-linear or non-smooth dynamic equations, and state-control inequality and equality constraints. A new generalized convexity condition for the dynamics and constraints is defined, and it is proved that this property, together with a constraint qualification constitute sufficient conditions for the strong Lagrange duality result and saddle-point optimality conditions for the problem. The discrete maximum principle of Pontryagin is obtained in a straightforward manner from the strong Lagrange duality theorem, first in a new form in which the Lagrangian is minimized both with respect to the state and to the control variables. Assuming differentiability, the maximum principle is obtained in the usual form. It is shown that dynamic systems satisfying a global controllability condition with convex costs, have the required convexity property. This controllability condition is a natural extension of the customary directional convexity condition applied in the derivation of the discrete maximum principle for local optima in the literature.

Vector-valued separation functions and constrained vector optimization problems: optimality and saddle points

In this paper, we consider a class of constrained vector optimization problems by using image space analysis. A class of vector-valued separation functions and a \begin{document}$ \mathfrak{C} $\end{document} -solution notion are proposed for the constrained vector optimization problems, respectively. Moreover, existence of a saddle point for the vector-valued separation function is characterized by the (regular) separation of two suitable subsets of the image space. By employing the separation function, we introduce a class of generalized vector-valued Lagrangian functions without involving any elements of the feasible set of constrained vector optimization problems. The relationships between the type-Ⅰ(Ⅱ) saddle points of the generalized Lagrangian functions and that of the function corresponding to the separation function are also established. Finally, optimality conditions for \begin{document}$ \mathfrak{C} $\end{document} -solutions of constrained vector optimization problems are derived by the saddle-point conditions.

Nucleation of crystal surfaces with corner energy regularization

The thermodynamics of strongly anisotropic crystalline surfaces is analogous to that of a binary mixture exhibiting phase separation. On a metastable planar surface, formation of stable orientations requires a nucleation process, in which the energy associated with the presence of corners must be considered. In this context, a nucleation event corresponds to the formation of a critical shape for the crystalline surface before the system enters the growth regime. We first derive the Euler-Lagrange equation for crystal surface nucleation, in two dimensions, and show that the saddle-point condition corresponds to a vanishing chemical potential along this critical surface. We then perform numerical simulation of the equation of motion for the crystal surface and show that, as compared with saddle point nucleation, ridge crossing is dynamically favoured.