Papers

We consider the intersection $\mathcal{Q}$ of $m$ equal-radius balls in $\mathbb{R}^n$ whose centers lie on a common sphere centered at the origin, with the origin contained in the convex hull of the centers. Assuming $\mathcal{Q}$ possesses a unique vertex $x^{\star}$ lying on the hyperplane $1_{n \times 1}^T \cdot x=0$, under a combinatorial-stability assumption, we give a polynomial-time algorithm that recovers $x^\star$. The algorithm constructs a one-parameter family of auxiliary polytopes (max-indicator polytopes) whose vertices move radially under a controlled translation of a quadratic center. The distinguished vertex remains fixed, while all others travel along fixed rays. By comparing a pair of symmetrically perturbed polytopes with the unperturbed one and solving a linear number of linear programs, the algorithm isolates $x^\star$.

We propose Dynamic Constraint Learning (DCL), a data-driven framework for constrained optimization when constraint functions are unknown and cannot be queried during optimization. At each iteration, the method learns a local surrogate from nearby data and solves a subproblem within a data-supported trust region. Compared with offline global constraint learning, the approach uses local surrogates that adapt to the data distribution during optimization and can achieve solution quality comparable to that of global models while using simpler local models and smaller optimization subproblems. We demonstrate the performance of DCL on a synthetic test problem and two case studies from the literature.

The optimal design of equilibrium particle distributions generated by external electric fields is investigated in the framework of stationary Maxwellian equilibria of the Vlasov-Poisson system. The resulting normalized Poisson-Boltzmann equation is analyzed, and existence and uniqueness of the self-consistent equilibrium potential are established by two complementary approaches. A Schauder fixed-point argument based on uniform estimates for the normalized nonlinear source and a variational approach based on a strictly convex free-energy functional are developed. Building upon these analytical results, a unified optimal design framework is formulated for equilibrium densities. Valley-potential, quadratic tracking, and Kullback-Leibler design criteria are treated within the same optimization framework. First-order optimality conditions are derived, and a projected nonlinear conjugate-gradient algorithm is proposed for the numerical solution of the resulting optimization problems.

Semidefinite relaxations of binary optimization problems often fail Slater's condition. Full facial reduction restores strict feasibility but may require auxiliary problems as costly as the original relaxation. We analyze partial facial reduction based on tractable inner approximations of the positive semidefinite cone: the nonnegative diagonal, diagonally dominant (DD), and scaled diagonally dominant (SDD) cones. For this class of SDP relaxations, we prove that every terminal face identified by SDD-partial FR is described by variables fixed to zero or one and groups of variables forced to be equal. The face admits a full-column-rank \(\{0,1\}\) basis matrix with disjoint column supports, yielding a natural reduced formulation that can preserve sparsity. For polyhedral inner approximations, we characterize the face exposed at each step through the inequalities active on the associated outer relaxation. We also establish sharp limitations. We construct a family with singularity degree two for which DD- and SDD-partial FR require the maximum possible number of steps. We further show that factor-width restrictions can prevent detection of dense valid affine equalities. These results characterize both the structure recovered by tractable partial FR and the cost of restricting the exposing-matrix search.

Machine learning demand forecasts optimize statistical accuracy yet leave excess operational volatility that inflates safety stock and amplifies the Bullwhip effect. We introduce Contextual Deconvolution (CD), a two-stage estimator that reframes demand sensing as a convex decomposition: a kernel-modulated banded operator separates transient promotion-driven shocks from a smooth structural baseline, and hierarchical partial pooling enables catalog-scale deployment without per-SKU training. The operator is data-derived, not imposed—it reduces to the identity wherever the promotional response is impulsive (most of M5, all of Favorita) and contributes only where genuine multi-day carryover exists, so the gains rest on the structural decomposition itself. Evaluating strictly out-of-sample on 30,490 M5 SKUs and 2,845 Favorita items, with calendar-aware baselines given CD's identical future calendar, we anchor the contribution on a full inventory-cost accounting: CD lowers safety stock, holding cost, and order variance but under-provisions event spikes, reducing total cost only when holding costs exceed $\sim$20% of stockout costs (95% CI $[17\%,25\%]$); otherwise it is an operational-stability and inventory-capital layer, not an expected-cost minimizer. Its accuracy contribution is reliability rather than central tendency: across eleven baselines, CD attains the lowest cross-sectional dispersion of per-SKU error and mis-forecasts by more than 200% on 0.8% of SKUs versus 9.9–20.6% for every baseline, ranking first on both in all four M5 draws. Because the Variance Ratio and std-based safety stock are minimized by any sufficiently smooth forecast, we treat them as diagnostics, not objectives. A supporting analysis shows the learned demand operators are non-normal, yet CD's compact parametric kernel matches their operational performance interpretably.

Data-driven model predictive control (MPC) using Koopman operator theory is a promising approach for constrained control of unknown nonlinear systems. While linear Koopman realizations are commonly used due to their simplicity, bilinear Koopman realizations can provide significantly higher approximation accuracy for nonlinear control systems. However, robust MPC (RMPC) formulations that account for modeling errors in bilinear Koopman realizations remain limited. This paper proposes a RMPC framework for unknown nonlinear systems with general nonlinear constraints based on data-driven bilinear Koopman realizations. A central difficulty is that finite-dimensional Koopman predictors need not preserve the manifold of valid lifted states, so multi-step prediction in lifted coordinates may leave the region where one-step error certificates apply. We address this issue by reprojecting each predicted lifted state back onto the manifold, thereby obtaining an error-aware discrete-time control-affine predictor in the original state space without impractical assumptions. For this predictor, we develop a discrete-time robust control contraction metric based homothetic tube construction, and then formulate a tube-based RMPC problem with terminal ingredients. Under the proposed formulation, we prove robust satisfaction of the original nonlinear constraints by the true closed-loop trajectory, recursive feasibility, and convergence to a neighborhood of the target state. Numerical experiments demonstrate robust stabilization of nonlinear systems and the advantages of the proposed method over existing Koopman-based RMPC approaches in terms of performance.

The conditional gradient method is attractive when linear minimization over the feasible region is substantially cheaper than projection. Its classical convergence theory, however, is formulated for compact feasible sets, whereas many natural convex feasible regions are closed and unbounded. This paper studies a simple compact-restriction principle for applying conditional gradient steps to unbounded feasible regions. The first scheme uses one compact convex set containing the initial objective sublevel set. The second scheme updates the restriction by intersecting compact convex sets generated along the iterations. In both cases the linear minimization oracle is solved only over compact subsets, but the resulting objective values converge to the global optimum of the original problem, provided the compact restrictions contain the corresponding objective sublevel sets. For smooth convex objectives we obtain the standard superlinear convergence rate of the objective. We also record constructive restrictions based on strong convexity, exact sublevel sets, and epigraph caps, and include a nonsmooth conditional subgradient extension with a sublinear convergence rate under a curvature assumption. Numerical experiments illustrate the behaviour of the fixed and dynamic restrictions on unbounded feasible regions.

In this paper, we introduce a minimal dynamical model for the diffusion of a new social norm, in which individuals transition among three states: non-supporters, silent supporters, and vocal advocates. Despite its simplicity and its close relation to the classical SI-type and SIS-type spreading dynamics, this model exhibits a nontrivial latent-outbreak dynamic pattern: an initial small adoption wave is followed by a long quiescent period and then an abrupt, endogenous explosion of support. Through a complete analytical characterization of equilibria, stability, convergence, and phase-transition conditions, we identify the nonlinear mechanism that generates this incubation phenomenon and derive estimates of the latent period. Our results reveal a dynamical route to sudden social change driven purely by internal interactions rather than external shocks.

There is no general method for designing proper parameters to achieve faster convergence in distributed optimization algorithms. In this paper, we consider the distributed inexact gradient tracking (DIGing) algorithm with the objective function being the unweighted sum of squares. By representing the iteration algorithm as a dynamical linear system, we decompose it into different graph frequencies and obtain a set of decoupled subsystems, on which we can easily analyze the convergence rate. By using Routh stability criterion from control theory, we derive the explicit formula of the optimal worst-case convergence rate and the corresponding parameters. We can see that the convergence rate of DIGing is slow even for the simplest objective functions, thus acceleration is necessary for general application. The proposed method can be viewed as the first step toward optimal parameter design of DIGing algorithm in solving general objective functions.

Seasonal population models must combine diffusion, inactive periods and abrupt biological transfers while remaining differentiable with respect to localized interventions. We formulate a finite-dimensional timing-and-placement control problem for a Stieltjes-time reaction–diffusion model and derive the exact adjoint of the fully discrete residual. Continuous finite elements and an implicit Stieltjes–Euler scheme represent propagation, compartment replacement, resets and historical phase averages. For the resulting box-constrained penalized problem, we prove well-posedness and differentiability of the discrete control-to-state map, existence of a minimizer, first-order stationarity, and equivalence between direct-sensitivity and adjoint gradients. Benchmark tests show machine-precision agreement (below $3\times10^{-15}$) and adjoint speed-ups from about $7.5$ to $30.9$ as the number of traps increases from one to four. The method is applied to Vespa velutina trap campaigns on a realistic Galicia mesh. Smoothed activation windows, domain-normalized moving kernels and the complete adjoint are independently verified. The final two-cluster control remains admissible and has a scaled gradient infinity norm of $2.97\times10^{-5}$ with 120 temporal subdivisions per month. Fixed-control evaluations at 60, 120 and 240 subdivisions yield a refinement-increment ratio of $0.4984$. At the finest level, the joint control outperforms time-only and reference controls, reducing the diagnostic objective by $0.49209%$. This reduction depends on uncalibrated trap-to-mortality intensities and should not be interpreted as field capture efficacy. The framework is fully reproducible, without claiming global optimality or a calibrated field-management prescription.

We present numerical results for the problem of minimizing the first Dirichlet eigenvalue of the Laplacian among three-dimensional convex polyhedra with a prescribed number of facets. This problem can be viewed as a polyhedral version of the classical Rayleigh$-$Faber$-$Krahn inequality. Using a parameterization of polytopes by supporting hyperplanes, the problem is first reformulated as an equivalent finite-dimensional constrained minimization problem. A Lagrangian framework is employed for the numerical solution. Under the assumption that each vertex is incident to exactly $d$ facets, the sensitivity analysis of the Lagrangian combines finite-dimensional perturbations of the facets of $d$-dimensional polyhedra with infinite-dimensional shape derivatives of both the eigenvalue and the volume. Depending on the number of facets, the numerical results yield well-known polyhedra, such as Platonic solids, prisms, and the tetrakaidecahedron, or reveal nonregular optimal shapes exhibiting several symmetries.

Fuel-dominant powered descent can be written as a convex program, but the usual full-state epigraph formulation still carries many state variables, fuel epigraph variables, and dynamics equalities. In addition, the pure-fuel objective provides no strong-convexity curvature. This paper combines three structural reductions. First, a dimensionally consistent low-weight energy term makes the control solution unique. Second, a terminal-state sensitivity recursion eliminates every intermediate state exactly; invertible row normalization turns the 30-node baseline with 300 primal variables and 174 equality multipliers into a problem with 90 control variables and six terminal multipliers. Third, the shared radial structure of the fuel norm and thrust ball gives an exact closed-form proximal operator consisting of group shrinkage followed by magnitude clipping. The condensed problem is solved with a fixed-budget extrapolated proportional–integral projected-gradient iteration implemented in fixed-size C17 arrays. In a Mars powered-descent case with energy weight 0.02 and relative reference-solution tolerance $10^{-3}$, the iteration count decreases from 2744 for the pure-fuel full-state epigraph baseline to 93, while the fuel metric increases by only 0.033%. The mean end-to-end solve time is \SI{68.2}{\micro\second}, and P99 is \SI{128.1}{\micro\second}, on an Intel i7-10875H. Because the thrust set in this test case is already a convex ball, the contribution is fast solution of the convex core rather than a new lossless-convexification theorem.

We study linear programming (LP) bounds for sphere packings that fiber over translates of a fixed lattice of lower rank, as well as their dual formulations. In doing so, we place the work of Conway and Sloane on fibered packings in the context of linear programming bounds for packing problems on $\mathbb{R}^k \times A$, where $A$ is a compact abelian group. For these programs we prove that the primal and the dual both attain their optima, so that every instance has an optimal pair satisfying complementary slackness. We study cases in which the LP bounds considered here achieve the best known sphere packing densities in dimensions $\le 9$ with prescribed translational symmetry, recovering analogues of Propositions 2, 3, 5, and 8 of Conway and Sloane, and we show that the Barnes-Wall lattice achieves the optimal sphere packing density for any $16$-dimensional packing that fibers over translates of $E_8$. In dimension $6$, we show that the natural LP bound fibering over translates of $D_4$ is in fact equivalent to the LP bound in dimension $2$ for ordinary sphere packing, while the LP bound for $4$-dimensional packings that fiber over $A_2$ translates is implied by the LP bound in dimension $2$ but has additional rigid structure that may make it more tractable. Finally, using the discrete reduction framework of Li, we show that the linear programming bound for packings fibering over translates of $A_3$ is strictly above $1$, so the linear programming bound alone cannot prove Conjecture 4.1 of Cohn and Rajagopal.

This work presents two different types of proximal gradient methods, with line search and without line search, for solving unconstrained set-valued optimization problems under the lower set-less ordering relation induced by a solid cone that is convex, pointed, and closed. The objective mapping of the problem involves finitely many functions, with each one being the sum of a continuously differentiable function and a convex function that is proper and closed. We present an approach to characterize weakly minimal points of the problem with the help of weakly efficient points of a family of vector optimization problems. Thereafter, we establish a stationarity condition along with its connection with weakly minimal points of the problem under study. Based on the stationary condition, the concept of a descent direction at a non-stationary point is discussed. In view of the line search-based method, we formulate an Armijo-type line search condition and establish the existence of such a step-size. For the proposed methods, global convergence is established under mild assumptions. The convergence analysis of the proximal gradient method with line search provides a theoretical advancement over the convergence results previously established for the steepest descent method in set-valued optimization problems. In addition, we analyze the computational complexity of the proposed methods and show that both methods achieve a convergence rate of $\mathcal{O}(1/\sqrt{k})$. Numerical results are reported to test the performance of the methods in practice.

This paper introduces a discrete-time event-triggered extremum seeking framework for real-time optimization of multivariable nonlinear systems. In contrast to conventional discrete-time extremum seeking implementations that rely on periodic input updates, the proposed scheme updates the control action only when a state-dependent triggering condition is met, enabling aperiodic execution and substantial reduction of actuation and communication effort. The resulting closed-loop architecture reconciles two structurally different paradigms: the periodic excitation required for gradient recovery in extremum seeking and the aperiodic philosophy of event-triggered control. By combining discrete-time averaging arguments with Lyapunov-based analysis, we prove practical convergence of the system trajectories to a neighborhood of the unknown extremum and exponential stability of the corresponding average dynamics despite the loss of uniform sampling. The results show that appropriately designed triggering rules preserve the essential optimization mechanisms of classical extremum seeking while drastically reducing the number of input updates. Numerical simulations demonstrate that comparable optimization performance can be achieved with significantly fewer actuation events, highlighting the suitability of the method for resource-aware digital and networked control implementations.

In this paper, we propose three projection–contraction algorithms for solving variational inclusion problems in the setting of Hilbert spaces, each incorporating a double inertial technique into the projection–contraction framework. The first algorithm \algoO achieves weak convergence under monotonicity and Lipschitz continuity of the single-valued operator, with an adaptive stepsize rule that does not require prior knowledge of the Lipschitz constant. A modified variant \algoT of \algoO attains $R$-linear convergence under the strong monotonicity assumption. The third algorithm \algoDIM obtains strong convergence to the minimum-norm solution without requiring strong monotonicity. We illustrate the proposed methods on an abstract variational inclusion problem and apply them to the split feasibility problem and the elastic net regularization problem, comparing them with existing methods in the literature. The $R$-linear convergence of \algoT is also verified through numerical simulations.

Distributed optimization becomes particularly challenging when nonconvex objectives are combined with nonlinear equality constraints: feasibility is network coupled, while most existing exponentially convergent methods rely on convexity or affine constraints. This paper introduces a feedback-linearization and singular-perturbation framework for two representative problem classes involving, respectively, distributed local nonlinear equality constraints and nonlinear aggregate equality constraints. The framework separates the regulation of consensus and feasibility residuals from optimization along the feasible manifold. Specifically, an ideal feedback-linearized dynamics is constructed whose output behavior can be explicitly assigned, while its zero-output dynamics coincides with the projected gradient flow of the aggregate objective on the feasible manifold. The ideal feedback-linearizing input, however, is determined by a state-dependent, network-coupled algebraic equation and is therefore not directly implementable in a distributed manner. To overcome this obstruction, we replace the nonlocal algebraic solution with a fast residual-tracking dynamics, yielding a singular-perturbation realization that uses only local and neighboring information. Under a local quadratic-growth condition imposed only on the feasible manifold, we establish local exponential convergence of both the ideal and distributed dynamics. For sufficiently strong time-scale separation, the convergence rate of the distributed realization can be chosen arbitrarily close to that of the ideal dynamics. Explicit Euler discretizations are also proved to preserve local exponential convergence. Under stronger manifold regularity conditions, the region of attraction extends to a tubular neighborhood of the feasible manifold and, for affine local equality constraints, to the whole admissible state space.

Climate models often feature nonlinear responses to changes in model parameters or boundary conditions. Factor separation asks how much of the resulting change should be assigned to altered factors and their interactions. Lunt et al. (2010) showed that two factor attribution methods, the linear-sum and shared-interaction methods, coincide for $n\le 4$ factors and conjectured this holds for arbitrary $n$. We show that the linear-sum and shared-interaction formulas are respectively the permutation and Harsanyi-dividend representations of the Shapley value. Their equality therefore follows from a classical theorem in cooperative game theory. To our knowledge, this is the first explicit identification of the Stein–Alpert and Lunt climate-model factor-separation methods with Möbius/Harsanyi coefficients and Shapley values. The result imports many established and efficient Shapley sampling methods into climate-model experimental design, and extends to vector-valued responses.

This study presents a theoretical analysis of partitional clustering on networks, analyzing both hard and soft assignment schemes with different objective functions. Cluster centers are not restricted to vertices but can also be located along the edges. We examine four key models: P-Median (PMP) and Sum of Squares Clustering (SSC) under hard assignment, and Probabilistic Distance Clustering (PDC) and Fuzzy C-Means (FCM) under soft assignment. Through mathematical analysis, we uncover structural properties that differentiate these models, such as the significance of assignment bottleneck points and the role of vertex-restricted solutions in determining optimal cluster centers. Our findings reveal that, while SSC and FCM can yield optimal centers along edges, PMP and PDC inherently favor vertex placement, leading to insights into clustering behavior on networks. These insights offer new directions for designing efficient algorithms and have implications ranging from facility location and network design to clustering on the embedding graphs that power similarity search in modern retrieval systems.

This paper assesses the readiness of Türkiye's electric vehicle (EV) charging infrastructure to support cost-competitive intercity travel compared to internal combustion engine (ICE) vehicles. Utilizing a directed acyclic graph-based scheduling framework, charging stops are optimized for over 2 million synthetic intercity EV trips across the national highway network, evaluating 9,641 licensed charging stations. An eight-scenario case study examines the interplay between charger power (5–350 kW) and station capacity (5 and 50 plugs), supplemented by a 200-run parametric sensitivity analysis on EV range, discharge rate, charger power, and state-of-charge (SoC) operating bounds. A three-tier economic analysis isolates operational costs, total cost of ownership (TCO), and infrastructure investments. Economically, EVs demonstrate a 23–26% operational cost advantage driven by Türkiye's favorable fuel-to-electricity price ratio, maintaining a 10–12% TCO advantage despite higher purchase prices. However, full-system infrastructure savings heavily depend on station sizing, yielding positive returns at 5 plugs per station but negative returns at 50. Ultimately, vehicle discharge rate emerges as the dominant economic lever. This study provides the first countrywide, vehicle-level assessment of intercity EV charging readiness for an emerging market, offering actionable insights for policymakers on infrastructure gaps, station sizing tradeoffs, and adoption incentives.

Safety inflation can cause nearby obstacles to overlap, violating the disjoint-obstacle assumptions used by many modulation-based reactive planners. We investigate Star-World workspace reshaping for three-dimensional reactive control of a Franka Emika Panda manipulator. At each update, intersecting inflated obstacles are clustered and replaced by star-shaped proxies before a dynamical-system-based end-effector controller is evaluated. A null-space artificial-potential-field term provides complementary arm-body avoidance. We compare reshaped and unreshaped obstacle representations in six PyBullet scenarios using goal attainment, path-length ratio, and computation time. In this preliminary 12-trial evaluation, reshaping reaches the goal in five of six scenarios, compared with four of six for the unreshaped baseline. It resolves the canonical overlapping-wall case and requires 0.68–8.70\,ms per workspace update for scenes containing one to seven obstacles. However, it also increases path length, produces near-equilibria in two cases, and closes a navigable corridor through over-aggressive merging. These results show both the promise and the practical limitations of transferring Star-World guarantees from workspace geometry to a redundant manipulator controlled through inverse kinematics.

Urban eco-driving requires the simultaneous planning of speed, acceleration, lane decisions, surrounding-vehicle safety, intersection rules, and vehicle energy use. Existing studies commonly optimize only selected aspects of urban driving, such as longitudinal motion, lane changing, intersection crossing, or powertrain energy management, while treating the remaining decisions separately. Unlike these studies, this paper develops an integrated finite-horizon eco-driving planning framework for urban hybrid vehicles that jointly optimizes traffic behavior and powertrain operation within a unified mixed-integer formulation. The novelty of the proposed method lies in simultaneously modeling longitudinal motion, lane occupancy and changes, car-following and lane-change safety, signalized and unsignalized intersection behavior, and hybrid powertrain operation. The formulation captures lane availability, lane-dependent speed limits, mandatory and emergency lane changes, safe-to-clear signal conditions, downstream clearance, stop-and-yield rules, engine and motor power, battery state of charge, regenerative braking, and fuel consumption. Simulation studies compare the proposed method with rule-following, signal-aware, overtaking-enabled, and kinematic-only optimization baselines. The results demonstrate improved energy performance while maintaining feasible, safe, comfortable, and traffic-rule-compliant urban driving plans.

Modern power grids are increasingly vulnerable to coordinated cyber-attacks, particularly false data injection attacks (FDIAs) that can evade conventional residual-based detectors. While most existing detection methods rely on instantaneous measurements, coordinated dynamic attacks can remain stealthy at each time step while introducing structured temporal deviations. This paper develops a joint framework for modeling and detecting such attacks in multi-area power systems. A time-aggregated attack model is first formulated to capture temporal evolution and inter-area coordination. For detection, a kernel-embedded functional subspace detection (KEFSD) method is proposed, which models residual trajectories in a reproducing kernel Hilbert space (RKHS) and employs RKHS-constrained functional principal component analysis (PCA) to identify anomalous temporal patterns. Simulation results on a modified IEEE 14-bus system demonstrates the proposed method achieves improved detection performance compared to the conventional residual based 2-norm detector.

The alignment of Small Language Models (SLMs) in the 70–500M parameter range using reinforcement learning is often considered unstable, though the underlying failure mechanisms have not been systematically investigated. In the State-of-the-Art (SOTA) research, fifteen (model, corpus) configurations were trained using Proximal Policy Optimization (PPO). The experiments included Pythia-70M, 160M, 410M and SmolLM2-135M, 360M on the TinyStories, CNN/DailyMail, and Wikitext-103 corpora. Three reproducible failure modes were identified in small-scale language models: silent LoRA parameter freezing in standard PEFT/TRL pipelines, numerical overflow in importance ratios when using bfloat16, and catastrophic policy collapse due to reward-model error. These issues were addressed using a merge-and-reinitialize adapter technique, float32 precision during PPO updates, and a three-layer safety mechanism comprising reward whitening, importance-ratio guarding, and weight rollback. In this paper, a capacity-headroom hypothesis is proposed, which states that PPO performance at the SLM scale depends on both a fluent supervised model ($\text{PPL}<20$) and a discriminative reward signal, rather than on the number of model parameters. The proposed system converged stably in all experiments and improved preference win rate over the SFT baseline in configurations with a fluent prior and an informative reward signal. Furthermore, it outperformed instruction-tuned baselines while requiring significantly less training data. All checkpoints, preference datasets, and training scripts are publicly released$^{\S}$.

Classical grasp quality metrics assume one deterministic friction coefficient and therefore cannot assess whether a grasp maintains force closure across plausible friction values. We present FIRMGrasp, a family of grasp quality metrics that incorporates friction uncertainty through Conditional Value-at-Risk (CVaR). At confidence level $\beta$, we evaluate the force-closure margin at the mean of the adverse friction tail. This evaluation defines the risk-adjusted margin $\varepsilon^{(\beta)}$, the inscribed-ball radius of the corresponding grasp wrench space. We prove that $\varepsilon^{(\beta)}$ varies monotonically with $\beta$, remains differentiable in the grasp parameters, and certifies that any grasp with $\varepsilon^{(\beta)} > 0$ achieves force closure with probability at least $\beta$. Across 1,599 LEAP Hand and Allegro Hand grasps, $\varepsilon^{(\beta)}$ identifies friction-sensitive grasps that receive high nominal Ferrari-Canny scores, and 53% of the nominally force-closed grasps lose closure in the adverse friction tail. The nominal margin ranks a successful grasp above a failed grasp with probabilities of only 0.53 in the shake test and 0.67 in the pick test, whereas $\varepsilon^{(\beta)}$ achieves 0.63 and 0.78. At an adverse friction coefficient of 0.2, 70% of grasps with positive $\varepsilon^{(\beta)}$ withstand a simulated lift and lateral pull, compared with 25% of grasps with positive nominal margin and nonpositive $\varepsilon^{(\beta)}$. We also synthesize grasps with positive $\varepsilon^{(\beta)}$ for the RealHand L6 and LEAP Hand, both of which retain the object during adverse-friction lifts. In MuJoCo trials with the RealHand L6, 95% of grasps that establish contact and have positive $\varepsilon^{(\beta)}$ retain the object at the same adverse friction coefficient.

A Derivative-Free Optimization (DFO) model is developed and analyzed for solving inverse structured symmetric matrix problems for which the eigenvalues are specified. Some (zero and nonzero) entries are preassigned and cannot be changed, while others should be nonzero but their values are not given. The rest of the entries are completely free. The obtained matrix must meet these criteria and have the specified eigenvalues. This specialized inverse eigenvalue problem is relevant to various applications and is linked to determining the graph, with weights on the undirected edges, of the matrix associated with its sparse pattern. Our novel optimization model requires computing the eigenvalues of a symmetric matrix to evaluate the non-differentiable objective function. We apply deterministic DFO schemes, specifically the global variant GLODS of the well-known family of directional direct search (DDS) methods. We discuss its convergence properties which are based on the fact that the objective function of our model is Lipschitz continuous. Additionally, we explore the potential benefits of using several well-established heuristic strategies to solve the proposed optimization model. We present preliminary numerical results to illustrate and compare the performance of the considered deterministic and heuristic DFO options in various possible scenarios.

Muscle redundancy can be formulated as a constrained selection on a time-varying convex set of feasible activations. We introduce Torque Fiber Proximal Dynamics (TFPD), where activation evolves as the Euclidean projection of the previous state onto a convex polytope defined by torque equality and physiological bounds. TFPD is equivalent to a backward-Euler discretization of a sweeping process and a variational inequality with a maximal monotone normal cone operator. Within this framework, antagonist recruitment emerges naturally under a projection-based control hypothesis as a structural consequence of active-set transitions induced by the geometry, not as an imposed cost. We derive sufficient KKT-based conditions for antagonist activation under generic moment-arm asymmetry, linking boundary projection, strict complementarity, and torque coupling. We validate TFPD on a three-muscle elbow model using exact quadratic programming, benchmark it against five classical methods and a random baseline, and achieve a Pearson correlation of 0.68-0.73 with triceps EMG envelopes across ten subjects without any tunable cost-weight parameters. Sensitivity analysis and a three-dimensional activation trajectory visualization demonstrate robustness. TFPD connects neuromuscular coordination to proximal point theory, variational inequalities, and projected dynamical systems.

This paper develops a functional H_infinity filter for nonlinear descriptor systems subject to external disturbances. Conventional H_infinity filtering approaches for descriptor systems impose restrictive regularity assumptions and employ implicit descriptor-form filters, leading to practical implementation difficulties. Moreover, existing approaches mainly target full-or reduced-order state estimation, which is computationally inefficient when only a specific functional of the state is required. To address these limitations, the filter is formulated directly in an explicit state-space framework and can be initialized with arbitrary initial values. The filter order is chosen to be less than or equal to the dimension of the functional vector to be estimated, thereby reducing computational complexity. The considered nonlinearities are characterized using incremental quadratic constraints parameterized by appropriate multiplier matrices, which encompass Lipschitz, one-sided Lipschitz, monotone, and many other nonlinearities. Sufficient criteria for the existence of the proposed filter are established through a rank condition imposed on the system matrices together with a set of linear matrix inequalities (LMIs). Under these conditions, asymptotic stability of the estimation error dynamics is guaranteed, while the influence of external disturbances on the error is bounded within a prescribed L2-performance framework. Finally, numerical simulations demonstrate and validate the effectiveness of our theoretical results.

Generative models are increasingly adopted in distributionally robust optimization (DRO), but existing approaches trade off model compatibility and adversarial structure: methods that accept arbitrary samplers do not restrict worst-case laws to a generator family, while generator-parameterized adversaries rely on model-specific access such as likelihoods, scores, or training data. We propose Generative Distributionally Robust Optimization (GDRO), a principled framework that accepts any sampleable conditional generator as the nominal model and restricts worst-case laws to a chosen conditional generator family. The key is the sampler-Sinkhorn pairing: samplers represent the conditional laws exactly, while Sinkhorn divergence compares their induced distributions without likelihood access and can be estimated from samples alone. The resulting population problem admits a direct finite-sample approximation and differentiable primal-dual implementation at the active decision context. For Lipschitz losses, the population Sinkhorn radius bounds downstream degradation. Across explicit and implicit generators, our method reduces rare-context inventory regret by 60% and SocialGAN navigation collisions by 50% relative to nominal decisions.

Differentiable simulation can accelerate contact-rich trajectory optimisation by exposing local sensitivities of task outcomes to controls. Existing approaches either use finite differences, which are expensive and step-size sensitive; differentiate iterative contact solvers by unrolling automatic differentiation (AD), which stores a growing computation trace; or require intricate, solver-specific KKT sensitivity derivations. We introduce an AD-assisted implicit derivative for regularised smooth contacts and apply it to Mujoco MJX, based on the Implicit Function Theorem (IFT). The method differentiates the stationarity residual at the tolerance-converged solution, avoiding both solver unrolling and hand-assembled KKT systems. IFT keeps compiled temporary memory nearly constant with solver effort, changing by less than 4$\%$ from one to ten iterations versus 10.6$\times$ growth for unrolled AD. IFT memory grows slower with active contacts and model dimension, using 20$\times$ less memory at 256 contacts and 6$\times$ less at 16 contacts and 96 DoF. We further introduce optimiser distillation for residual MPC, amortising batched full-horizon iLQR into a policy that guides short-horizon residual iLQR. Across Finger, Franka, and Unitree, this raises six-step success by 28-98 percentage points over standard iLQR.