The rise of social media has significantly influenced digital marketing, especially within the
IT Channel Industry. Channext, a key player in automating channel marketing, faces the
challenge of achieving optimal through-partner social engagement, which includes maximiz-
ing shares, impressions, clicks, likes, and comments on social media posts. However, the
current approach is hindered by content overload, leading to scheduling conflicts and reduced
engagement.

To address this issue, this project introduces an advanced model for the vendor-partner net-
work, with several key contributions:
• Predictive Analytics: A robust predictor is developed to capture the relationship be-
tween historical impressions data and social post characteristics, aiming to forecast
future impressions with improved accuracy.
• Optimized Scheduling: Predictive insights guide the scheduling model to maximize
impressions while minimizing post overlaps and balancing visibility across the network.
• Real-Time Adaptation: The model incorporates real-time impressions data to refine
predictions, enabling the system to dynamically adjust to fluctuations in engagement
trends.
• Future Expansion: Building on this model, the goal is to integrate a learning system
based on the linUCB algorithm, balancing exploration and exploitation to allow the
model to better adapt to the evolving dynamics of social media.

Driven by carefully selected data and machine learning techniques, this framework aims to
enhance through-partner social engagement. By empowering IT partners to actively participate in vendor-led social media campaigns while avoiding over-publishing, we seek to optimize
engagement strategies.
Furthermore, the integration of a learning framework aims to enable the project to autonomously adapt to changes in the vendor-partner network’s dynamics. Achieving this level of adaptability will require the development of methods to handle non-stationary environ-
ments, where engagement patterns evolve over time. With these advancements, the project
could set a new standard for intelligent, responsive marketing strategies in the IT channel
industry. Moving beyond traditional automation, this project envisions a finely tuned system
capable of sustaining impact in the rapidly evolving digital landscape.

In this thesis, we study Online Convex Optimization algorithms that exploit predictive and/or dynamical information about a problem instance. These features are inspired by recent developments in the Online Mirror Decent literature. When the Player's performance is compared with the best fixed decision in hindsight, we show that it is possible to achieve constant regret bounds under perfect gradient predictions and optimal minimax bounds in the worst-case, generalizing previous results from the literature. For dynamic environments, we propose a new algorithm, and show that it achieves dynamic regret bounds that exploit both gradient predictions and knowledge about the dynamics of the action sequence that the Player's performance is being compared with. We present results for both convex and strongly convex costs. Finally, we provide numerical experiments that corroborate our theoretical results.

A quadrotor is a type of Unmanned Aerial Vehicle that has received an increasing amount of attention recently with many applications being actively investigated. Possible applications include search and rescue, surveillance, supply of food and medicines in emergency situations and object manipulation in construction and transportation. An interesting subproblem of load transportation is the control of the position of a cable suspended load. The challenge is in the fact that the quadrotor-load system is highly nonlinear and under-actuated. The load cannot be controlled directly and has a natural swing at the end of each quadrotor movement.
The goal of this thesis is to present a nonlinear geometric control approach, investigate its possibilities and limitations to track the position of a cable suspended load. The focus lies on the quadrotor-load subsystem where the cable tension is non-zero, which is analogous to modeling a rigid link between the quadrotor and load.
After introducing the basic concepts, an introduction is given on geometric mechanics. This differential geometric based approach is used to model and control the system, based on the geometric properties of the system dynamics. It is shown how the configuration of the quadrotor-load system can be described on smooth nonlinear geometric configuration spaces, and analyzed with the principles of differential geometry. This allows for modeling in an unambiguous coordinate-free dynamic fashion, while avoiding the problem of singularities that would occur on local charts.
Next, the geometric properties are utilized to define tracking error functions on these same spaces, making it possible to design almost-globally defined nonlinear geometric controllers.
A backstepping approach is applied to generate a cascaded structure with multiple nonlinear geometric controllers, allowing control of several flight modes that are responsible for the control of 1) quadrotor attitude, 2) load attitude and 3) load position.
Finally, simulations demonstrate the stability and abilities of the nonlinear geometric controller. A Linear Quadratic Regulator is derived to compare both of the control performances.
The tracking performances of both controllers are discussed for three different experiments.
From the results of the experiments can be concluded that the nonlinear control design allows control of multiple states with the final objective of controlling the load position. The nonlinear geometric approach proves effective in load position tracking of complex trajectories and
fast maneuvers, while maintaining stability of the closed-loop system.